Vol. II · 转型路径专题 专为非CS背景转入AI而作 · April 2026

从其他专业
转型 AI 的
路径调研

基于 Vol. I 中筛选出的 25 所美国 AI 先驱大学完整版。围绕三个深度问题展开：AI 项目的核心专业课与 listed faculty、非 CS/AI 系硕士生选 AI 课的政策、与 AI 交叉的硕士项目（涵盖统计/数学/生物/化学/物理/商科 6 个领域）。Top 8（CMU、MIT、Stanford、Berkeley、Georgia Tech、UIUC、Cornell、Princeton）数据已经多轮验证；Tier 2-3（17 所，含 9-25 排名校）经过逐校官方页面查证，无法在官方页面证实的内容已删除（部分领域显示为"弱/无 master 通道"是真实结论，不是数据缺失）。请以各校最新官方信息为申请时的最终依据。
Scope
8 所大学 × 3 个深度问题
Cross-Fields
Stat / Math / Bio / Chem / Phys / Biz
Sources
官方 catalogue + 项目主页
Cutoff
2025–2026 academic year
1

核心 AI 专业课与 Faculty

从每所学校官方课程目录抓取的"AI 专业课"，定义为：核心 ML/AI 主修课 + 项目明确列入的 AI 方向选修。课程为 graduate-level（4000+/6000+/200-level 取决于学校）。Faculty 名单来自项目官网的 listed/affiliated faculty 页面。

2

非 CS/AI 系硕士生选 AI 课的政策

本节聚焦研究生（master）层面，不涉及本科生。分两个维度：(a) 硬性门槛——非 CS/EE 系的 master 学生想选 AI 研究生课，需要哪些先修课、是否需要 instructor permission、是否限定本系学生优先入学；(b) 学位计算——选了 AI 课能否算入自己 master degree 的学分要求。

3

交叉硕士项目分析

对每所学校在 6 个领域（Stat/Math/Bio/Chem/Phys/Biz）寻找与 AI 交叉的 master's program。记录：(a) US News 在该 field 的排名，(b) 课程重合度——能算入学位要求的核心 AI 课数量，(c) 师资重合度——同时在 AI 项目和该交叉项目挂名的 faculty 数量。

关于精确度的说明："课程重合度"和"师资重合度"在大多数学校并未公开发布精确数字。本报告基于公开课表 + 项目网站的交叉引用做定性评估（强/中/弱），辅以可核实的具体例子。任何数值百分比都是基于可观察样本的估算，原始链接附在每节末，便于自行核查。Faculty 名单只列示例性的 6–10 位，并非完整名单。

I.Top 8 学校逐校解读

按 Vol. I 综合排名 · 1–8 · 高度验证
01

Carnegie Mellon University

卡内基梅隆大学 · 全美第一个本科 AI 学位（2018）的开设者，AI 研究全球 #1
USNews CS #1
①

AI program 核心专业课 & Listed Faculty

MSAII · BSAI · MS AIE

CMU 拥有美国最丰富的 AI 学位生态：本科 BSAI（SCS 内）、硕士 MSAII（语言技术学院 LTI 主导，工程方向）、MS in AI Engineering（工程学院）、MS Machine Learning（ML 系）、MS Computational Data Science 等。下面以 MSAII Knowledge Requirements + 核心选修为代表列出主修 AI 课。

11-695AI Engineering
11-651AI and Future Markets
11-654AI Innovation (Capstone)
11-747Neural Networks for NLP
11-777Multimodal Machine Learning
11-755ML for Signal Processing
11-641Machine Learning for Text Mining
10-605Machine Learning with Large Datasets
10-716Advanced ML: Theory & Methods
10-608Conversational Machine Learning
10-301/601Introduction to Machine Learning
15-780Graduate Artificial Intelligence
16-720Computer Vision
16-824Visual Learning and Recognition
16-725Medical Image Analysis
15-688Practical Data Science

Listed Faculty（MSAII 主导教师，节选）：

Jamie Callan Justine Cassell Alexander Hauptmann Florian Metze Eric Nyberg (MSAII Director) Bhiksha Raj Carolyn Rosé Roni Rosenfeld (ML Dept Head) Michael Shamos

BSAI 还跨 CSD、HCII、LTI、ML、Robotics、S3D 六个系，整体 listed faculty 超过 100 人——这是 CMU 的关键优势。

②

非 CS/AI 系硕士生选 AI 课的政策

高门槛 · 多层 permission
A · 硬性门槛（先修 / Permission）

核心规则："Graduate students must have the permission of their department"（CMU 课程目录原文）——任何 CMU 在读硕士想跨系选课，都需要本系研究生主任的批准，再经课程开设方批准。

SCS 自己的 MSAII 项目 FAQ 明确指出："Each school always gives priority to its own students over students from other schools within the university"——也就是说 Heinz College / Tepper 等其他学院的硕士生想注册 SCS 的 AI 课程（如 10-601/10-701 ML、11-747 Neural Networks for NLP），必须先排在 SCS 内部学生之后，capacity 满了就进不去。

实例：MSCF（Master in Computational Finance，跨 Stat/Heinz/Tepper/CS/Math 联合）的 ML I 课程明文写："Non-MSCF students may not take this course without written permission from the instructor." 这种"项目内部专属"的政策在 CMU 普遍。

另外 PCHE Cross-Registration（跨匹兹堡高校选课）每学期最多只能跨 1 门——这是 CMU 内部跨系选课无关，但反映了 CMU 整体偏严的选课文化。

B · 学位计算（能否算入自己 master degree）

CMU 各硕士项目的 elective 列表通常明确穷举可选课程——如 ECE 的 MS in AI Engineering 列出可计入 elective 的部门有 SCS（02 CompBio / 15 CS / 10 ML / 11 LTI / 16 Robotics）+ Tepper + Heinz——也就是从设计上鼓励跨系选 AI 课算学位。

反过来，从其他学院的硕士项目（如 Heinz MISM、Tepper MSBA）想把 SCS 的 AI 课算入学位则需要走两步：先获得 SCS 课程 instructor 批准入学，再获本项目 advisor 批准课程算入 elective。CMU 的灵活性源于电子化的 Degree Audit + 明文跨学院 elective 列表，但实际能否拿到课位取决于 capacity。

来源：cmu.edu/policies/student-and-student-life/cross-college-university-registration · msaii.cs.cmu.edu/frequently-asked-questions · cmu.edu/mscf/academics/curriculum/46926-statistical-machine-learning-i · ece.cmu.edu/academics/ms-ai/standard-program
③

与 AI 交叉的硕士项目（6 领域）

CMU × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。标签含义：●重合=同一课程在两个项目都算学分；●等价=不同代码但内容等价、官方互认；●elective=作为可选选修允许；●独有=仅在该项目出现。Faculty 标签：●primary=AI 项目的核心教师；●joint=同时是两个项目的核心；●affiliated=挂名/合聘。

交叉领域 项目名称 US News 排名 课程重合度 师资重合度
Stat
统计
Master of Statistical Practice (MSP)
Master of Applied Data Science (MADS)
※ Stat & ML Joint 仅 PhD 层面
Dept of Statistics & Data Science
Stat #6
USNews
 中
MADS 课表局限本系≈ 50%
 强
Stat & ML 系师资联合
课程重合详情
CMU MSAII / MS ML 与 MSP/MADS 课程交集
全部 重合 elective 独有
课号课程类型
10-601Intro to Machine Learning重合
36-700Probability & Math Stat重合
36-705Intermediate Statistics重合
10-605ML with Large Datasetselective
36-462Data Miningelective
11-747Neural Networks for NLP仅 AI 项目
36-755Advanced Statistical Theory仅 Stat 项目

注：MADS 项目政策——"typically not permitted to take courses outside of the Department of Statistics & Data Science"——因此实际重合率受限。MSP 较灵活，可以有 instructor 批准选 ML 课。

师资重合详情
同时活跃于 AI 项目与 Stat MS 项目的教师
全部 joint affiliated
姓名主要方向关系
Larry WassermanUPMC University Professor of Statistics & Data Science + ML Department; Member NAS; statistical ML + nonparametric inference + LLM evaluation; founding co-EIC Foundations and Trends in StatisticsStat&DS + ML, NAS
Aaditya RamdasAssociate Professor Stat&DS (75%) + ML (25%); inaugural COPSS Emerging Leader Award + IMS Peter Gavin Hall Early Career Prize + Sloan Fellow + NSF CAREER; sequential / valid inference + MLStat&DS + ML, COPSS
Sivaraman BalakrishnanProfessor Stat&DS; high-dim stats + ML; JASA + JRSSB Associate Editor; Amazon Research Award + Google Research Scholar; AI-SDM affiliatedStat&DS + ML
Pradeep RavikumarProfessor Machine Learning Department (SCS) — joint Stat-ML PhD adviser; probabilistic ML + graphical modelsML primary, Stat&DS joint
Jing LeiProfessor Stat&DS; conformal prediction + high-dim inference; statistical machine learning research area leadStat&DS primary
Edward KennedyAssociate Professor Stat&DS; causal inference + ML; double machine learning researchStat&DS primary
Cosma ShaliziAssociate Professor Stat&DS + ML adjunct; teaches Advanced Data Analysis + statistical learning theoryStat&DS primary, ML adjunct
Rebecca NugentStephen E. and Joyce Fienberg Professor + Department Head Stat&DS; founding director Corporate Capstone Program; high-dim clustering + classificationStat&DS Head, Fienberg Chair
Robert KassMaurice Falk Professor of Statistics & Computational Neuroscience; NAS Member; Bayesian methods + ML for neural dataStat&DS, NAS, Falk Chair
Kathryn RoederUPMC University Professor of Statistics and Life Sciences; NAS Member; statistical genomics + MLStat&DS, NAS
Mikael KuuselaAssociate Professor Stat&DS; STAMPS Co-director; ML for physical sciences + uncertainty quantification + CERN CMS Stat CommitteeStat&DS + STAMPS, NSF AI Physics
Ann LeeProfessor Stat&DS + ML affiliated; STAMPS Co-director; ML for high-dim physical-science data; NSF AI Physics InstituteStat&DS + ML, STAMPS
Max G'SellAssociate Teaching Professor Stat&DS; teaches data mining + post-selection inference + MLStat&DS primary
Roni RosenfeldProfessor of CS + Stat&DS affiliated; Delphi Group Director (CDC Center of Excellence for Influenza Forecasting); statistical ML + epidemic forecastingCS + Stat&DS, Delphi Director
Bryan WilderAssistant Professor ML + Heinz; AI for societal decision making + multi-agent MLML + Heinz
Hoda HeidariK&L Gates Career Development Assistant Professor in Ethics & Computational Tech; ML + Society + algorithmic fairnessML + S3D
Jeff SchneiderResearch Professor ML; Auton Lab Director; reinforcement learning + autonomous systemsML primary, Auton Lab

CMU Stat&DS 系（Dietrich College, 38 位 tenure-track faculty）+ Machine Learning Department (SCS, 2006 年世界首个 ML 系) 通过 Joint PhD in Statistics & Machine Learning 形成 CMU 独有的"Stat-ML"双系架构。Stat&DS 系研究领域主页明确列出 11 个方向: AI-SDM, Statistical ML, NLP & LLMs, Graphical Models & Networks, High-Dim Stats, Optimization, Optimal Transport, Causal Inference, Computational Neuroscience, Genomics, Bio-Epidemiology。STAMPS（Statistical Methods for the Physical Sciences）由 Kuusela + Lee 共同领导, 是 NSF AI Planning Institute for Data-Driven Discovery in Physics 的统计核心。
Math
数学
MS in Computational Finance (MSCF)
※ 无独立 Math MS
MSCF 跨 Math/Stat/Tepper/Heinz/CS 5 系联合
CMU 数学非顶级
Math #25+
USNews
 中
MSCF 含 ML 主线≈ 45%
 弱
数学系自身 AI 师资有限
课程重合详情
MSCF 中明确的 ML 主干课
全部 重合 elective 独有
课号课程类型
46-926Statistical ML I (MSCF 必修)重合
46-929Statistical ML II重合
10-601Intro Machine Learningelective
46-921Financial Optimization仅 MSCF
46-944Machine Learning II仅 MSCF（注：Non-MSCF 不可选）
11-747Neural Networks for NLP仅 AI 项目

注：MSCF 的 46-926 ML I 明文写"Non-MSCF students may not take this course without written permission from the instructor"——双向限制。

师资重合详情
MSCF / Math 系参与 AI 教学的教师
全部 joint affiliated
姓名主要方向关系
Nicholas BoffiAssistant Professor Mathematical Sciences + Machine Learning Department joint; Generative AI + flow maps + diffusion models + LLM; previously Courant + Google Brain + Harvard; develops principled methods for generative modeling at applied math/ML interfaceMath + ML joint
Dejan SlepčevProfessor + MCS Associate Dean for Faculty and Graduate Affairs; optimal transport + graph-based ML / SSL; PDE methods for ML clustering + dimensionality reductionMath primary, MCS Assoc Dean
Konstantin TikhomirovAssociate Professor Mathematical Sciences; random matrix theory + high-dim probability with ML applications; concentration inequalities for ML theoryMath primary
Gautam IyerProfessor + Associate Director Center for Nonlinear Analysis; numerical PDE + stochastic methods adjacent to ML scientific computingMath primary, CNA
Andrej RisteskiAssociate Professor ML Department; previously Norbert Wiener Research Fellow Applied Math + IDSS at MIT; ML theory + sampling + diffusion + LLM theoryML + Applied Math

CMU Mathematical Sciences (Mellon College of Science, 约 35 tenure-track) 是纯数学 + 应用数学 + 数学金融结合的部门, 与 ML 系强桥梁是 Boffi 同时持 ML 系 joint appointment（generative AI + flow maps），Risteski 之前在 MIT 应用数学+IDSS 做 Norbert Wiener Fellow，现在 ML 系。Slepčev 的 optimal transport + graph SSL 是经典数学桥梁。Center for Nonlinear Analysis (CNA)由 Irene Fonseca (Kavčić-Moura University Professor) 任 director, Gautam Iyer 任 associate director。
Bio
生物
M.S. in Computational Biology (MSCB)
M.S. in Automated Science: Biological Experimentation (MSAS)
联合 Mellon CoS + SCS（Lane CBD）
CompBio Top 5
领域内顶级
 强
必修即 ML≈ 65%
 强
CBD 是 SCS 内部
课程重合详情
MSCB 必修和 elective 中的 AI/ML 课
全部 重合 elective 独有
课号课程类型
02-620Machine Learning for Scientists（MSCB 必修）重合
02-712Biological Modeling重合（CBD 与 ML 系共同设计）
02-710Computational Genomics重合
10-605ML with Large Datasetselective
11-747Neural Networks for NLPelective
02-718Computational Medicineelective
03-711Genomics（湿实验侧）仅 MSCB
02-750Automation of Scientific Research仅 MSAS

CBD（Computational Biology Department）就在 SCS 之下，与 ML 系是兄弟部门——课程交叉密度高于其他学校的"AI×Bio"项目。

师资重合详情
同时活跃于 MSCB 和 CMU AI 项目的教师
全部 joint affiliated
姓名主要方向关系
Carl KingsfordHerbert A. Simon Professor in Ray and Stephanie Lane Computational Biology Department; Director Center for Machine Learning and Health; ML + diffusion transformers for molecular docking + genome algorithmsLane CompBio, Simon Chair, ML&Health Director
Ziv Bar-JosephFORE Systems Professor Lane Computational Biology Department + Machine Learning Department joint; HuBMAP NIH Center PI; ML for systems biology + single-cellLane CompBio + ML, FORE Chair
Jian MaRay and Stephanie Lane Professor of Computational Biology; ML for 3D genome (Higashi/scGHOST series, Nat Methods/Genet/Biotechnol); spatial transcriptomics MLLane CompBio, Lane Chair
Russell SchwartzProfessor Lane Computational Biology Department + Biological Sciences; ML in genomics + cancer evolution; NSF AI Physics Planning Institute memberLane CompBio + BioSci
Andreas PfenningAssociate Professor Lane Computational Biology Department; genomics ML + neuroscience deep learningLane CompBio primary
Robert F. MurphyRay and Stephanie Lane Professor of Computational Biology Emeritus + ML Department + Biomedical Engineering; founding department head; active learning of cell organization (Sloan Fellow, Microsoft Faculty Fellow)Lane CompBio Emeritus + ML + BME
Seyoung KimAssociate Professor Lane Computational Biology Department + Machine Learning Department; ML for genomics + graphical modelsLane CompBio + ML
Hosein MohimaniAssociate Professor Lane Computational Biology Department; ML for natural product discovery + mass spectrometryLane CompBio primary
Irene KaplowAssistant Professor Lane Computational Biology Department; ML for vertebrate phenotype evolution + enhancer activity predictionLane CompBio primary
Olexandr IsayevCarl and Amy Jones Professor of Chemistry + Lane Computational Biology Department affiliated; 22K+ citations; ML for drug discovery + AIMNet2 neural network potentials + GenAI for inverse molecular designChem + Lane CompBio, Carl Amy Jones Chair
Aarti SinghProfessor Machine Learning Department + Director, AI Institute for Societal Decision Making (AI-SDM); ML for biology + active learning + cell organizationML primary, AI-SDM Director
Eric XingRaj Reddy Professor ML + LTI + CS; petuum.ai founder; bio NLP + LLM; statistical ML for genomics; previously Mohamed bin Zayed University AI PresidentML/LTI/CS, Raj Reddy Chair
Chem
化学
无独立 AI×Chem master
MS in Chemical Engineering（含 ML for Materials）
MSAS（含化学相关方向）
化学系硕士极少
Chem #25+
非顶级
 弱
仅个别选修< 20%
 弱
无明确交叉聘任
课程重合详情
化学/化工 master 中可触及的 AI 课
全部 elective 独有
课号课程类型
10-601Intro Machine Learning（开放给非 SCS 但有 prereq）elective（需 advisor 批准）
02-620ML for Scientists（更适合化学背景）elective
06-625Chemical & Reactive Systems Modeling仅 ChemE
09-509Computational Chemistry仅 Chem

Ray and Stephanie Lane Computational Biology Department (CBD)是 SCS 七系之一, 2007 年由 Robert Murphy 创立, 2009 年成系, 2023 年命名为 Lane Computational Biology Department（首个 CMU 命名学术系）。MS in Computational Biology (MSCB) 是与 Department of Biological Sciences 联合的 master 项目。Lane CompBio 与 ML Department + Biological Sciences + Biomedical Engineering + Chemistry 跨系合作密集。Kingsford 同时任 Center for Machine Learning and Health 主任。

师资重合详情
CMU 化学相关 AI/ML 教师
姓名主要方向关系
Olexandr IsayevCarl and Amy Jones Professor of Chemistry + Lane Computational Biology Department affiliated; 22K+ citations; ML for drug discovery + AIMNet2 neural network potentials + AutoML/RL agents for molecular discovery + GenAI inverse molecular design; ACS Emerging Technology Award; NVIDIA GPU computing awardChem primary, Carl Amy Jones Chair, Lane CompBio
David YaronProfessor of Chemistry; Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians; deep RL for polymer synthesis (with Geoff Gordon ML); semiempirical Hamiltonians as ML modelsChem primary
Newell R. WashburnProfessor of Chemistry + Materials Science and Engineering; hierarchical machine learning algorithm for accurate models from small datasets; ML-driven biobased polymer + cement designChem + MSE
Hosein MohimaniAssociate Professor Lane Computational Biology Department; ML for natural product discovery + chemical mass spectrometry analysisLane CompBio + Chem-adjacent

CMU Chemistry 系（Mellon College of Science）AI/ML 转向以 Olexandr Isayev (2020 年加入, 后晋升 Carl and Amy Jones Professor) 为旗舰 PI——AIMNet2 neural network potentials 覆盖 14 元素 + 20M DFT 训练、Nature Communications/Chemical Science 期刊论文多篇、与 Princeton 合作发现可结晶有机半导体。Yaron 早期与 ML 系 Geoff Gordon + Tom Kowalewski 合作 ATRP 聚合 deep RL。Washburn 自创 hierarchical ML 算法 (HML) 用于小样本下的精确预测。化学背景学生进 AI 通道实质：CMU Chem PhD 选 ML PI（如 Isayev）即可。
Phys
物理
无独立 AI×Phys 项目
MS Physics（无 AI track）
研究路径走 ML PhD
物理系硕士极少
Phys #25+
非顶级
 弱
需自行拼凑≈ 25%
 弱
物理系无主流 AI 师资
课程重合详情
Physics master 路径中可碰到的 AI 课
课号课程类型
10-601Intro ML（需 advisor 批准）elective
10-707Advanced Deep Learning（需 prereq）elective
33-XXXPhysics 系核心课仅 Phys

CMU 没有"AI×Physics"专门项目，物理 master 想转 AI 主要靠自选 ML 课作为 elective，且需多重 permission。建议直接申 MSML 或 MSAII（welcome 各种背景）。

师资重合详情
姓名主要方向关系
Mikael KuuselaAssociate Professor of Statistics & Data Science + STAMPS Co-director; NSF AI Planning Institute for Data-Driven Discovery in Physics member; ML for particle physics (CERN CMS Statistics Committee); model-agnostic searches of new physicsStat&DS + STAMPS, NSF AI-Physics
Ann LeeProfessor Stat&DS + ML affiliated + STAMPS Co-director; NSF AI Physics Institute member; ML methodology for high-dim physical-science data + simulations in astronomy / particle physicsStat&DS + ML + STAMPS
Larry WassermanUPMC University Professor Stat&DS + ML; NAS Member; NSF AI Physics Institute member; statistical ML for physics applicationsStat&DS + ML, NAS, AI-Physics
Rachel MandelbaumProfessor of Physics + McWilliams Center for Cosmology; ML for weak gravitational lensing + cosmology surveys (Rubin/LSST); NSF AI-Physics InstitutePhysics primary, AI-Physics Inst
Hy TracAssociate Professor of Physics; cosmological N-body simulations + ML emulators; NSF AI-Physics InstitutePhysics primary, AI-Physics Inst
Tiziana Di MatteoProfessor of Physics; large-scale cosmological simulations + ML for galaxy formation; NSF AI-Physics InstitutePhysics primary, AI-Physics Inst
Scott DodelsonDepartment Head Physics; cosmology + ML for weak lensing surveys; NSF AI-Physics InstitutePhysics Head, AI-Physics Inst
John AlisonAssistant Professor of Physics; LHC ATLAS experiment + ML for particle reconstruction; NSF AI-Physics InstitutePhysics primary, AI-Physics Inst
Manfred PauliniProfessor of Physics; CMS Experiment particle physics + ML for jet tagging; NSF AI-Physics InstitutePhysics primary, AI-Physics Inst

CMU 无独立物理 ML master, 但 NSF AI Planning Institute for Data-Driven Discovery in Physics（由 MCS 主导, 跨 Physics + Stat&DS + ML + Lane CompBio + PSC）是物理生进 AI master 的核心 PI 集群。STAMPS Research Center（Kuusela + Lee 共同领导）是 Stat&DS 系内的物理科学统计/ML 子部门。物理生进 AI 实际路径：MS in Statistical Practice (MSP, Stat&DS 系) 选 STAMPS 方向, 或 PhD in Physics 走 NSF AI-Physics Institute 方向。
Biz
商科
MS in Business Analytics (MSBA, Tepper)
MS in Information Systems Mgmt (MISM, Heinz)
MSAII + Tepper MBA 双学位
MSCF（含 AI 重）
Tepper Biz Analytics #2
Biz Analytics #2
USNews 2026
 中
核心含 ML≈ 50%
 中
部分共聘
课程重合详情
Tepper MSBA / Heinz MISM / MSCF 中的 AI 课
全部 重合 elective 独有
课号课程类型
95-865Unstructured Data Analytics（Heinz）重合
46-926Statistical ML I（MSCF, 跨 Stat/ML）重合
10-601Intro Machine Learningelective（需 director 批准）
11-695AI Engineering（MSAII 必修）elective（仅 MSAII+MBA 双学位）
45-927Operations Strategy（Tepper）仅商科
94-XXXHeinz Public Policy 系列仅 Heinz
师资重合详情
Tepper / Heinz 与 SCS AI 项目共聘教师
全部 joint affiliated
姓名主要方向关系
Param Vir SinghAssociate Dean Research, Tepper + Carnegie Bosch Professor of Business Technologies and Marketing; digital economy + ML for sharing economy / fin-tech / crowdsourcing / social media; big data MLTepper Bus Tech, Carnegie Bosch Chair
Kannan SrinivasanH.J. Heinz II Professor of Management, Marketing and Business Technologies; ISS Distinguished Fellow; ML for marketing + structural modelsTepper Marketing, Heinz II Chair
Tim DerdengerDirector, Center for Intelligent Business (CIB) + Associate Professor of Marketing and Strategy; research initiatives in generative AI + collaborative AI + distributed ledgersTepper Mktg, CIB Director
R. RaviAndris A. Zoltners Professor of Business + Professor of OR + CS; INFORMS Fellow; analytics strategy + optimization + ML; MBA Teaching AwardTepper OR + CS, Zoltners Chair, INFORMS Fellow
Zachary LiptonRaj Reddy Associate Professor of Machine Learning + Tepper joint; ACMI Lab Director; Cofounder & CTO Abridge (healthcare AI); ML methods + theory + healthcare + NLPML primary, Tepper joint, Raj Reddy Chair
Alan MontgomeryProfessor of Marketing + Machine Learning Department joint + Research Director PNC Center for Financial Services Innovation; Bayesian statistics + clickstream analysis + ML for financeTepper Mktg + ML
Willem-Jan van HoeveSenior Associate Dean + Carnegie Bosch Professor of Operations Research; discrete optimization + decision diagrams + 2× Google Faculty Research AwardsTepper OR primary, Senior Assoc Dean
Karan SinghAssistant Professor of Operations Research; online learning + RL theory + adversarial banditsTepper OR primary
Yan HuangAssociate Professor of Business Technologies; analytics for sharing economy / digital platforms / generative AI experimentsTepper Bus Tech primary
Tridas MukhopadhyayDeloitte Consulting Professor of e-Business; IT economics + ML for e-business analyticsTepper Bus Tech, Deloitte Chair
Leman AkogluHeinz College Professor + courtesy in CS Department + Machine Learning Department; graph mining + anomaly detection MLHeinz + ML, CS courtesy
Michael D. SmithHeinz College + Tepper joint Professor; 2024 INFORMS ISS Distinguished Fellow; ML for entertainment industry analyticsHeinz + Tepper, INFORMS ISS Fellow

Tepper School of Business MSBA（9 个月 STEM-designated）+ Heinz College MISM Business Intelligence and Data Analytics (BIDA)是 CMU 商科 AI 双通道。Tepper Center for Intelligent Business (CIB) 由 Derdenger 领导, 研究 generative AI + collaborative AI + 分布式账本。Tepper 教师与 ML Department 跨系联合 (Lipton/Montgomery)。Param Vir Singh 任 Tepper 研究副院长 + Carnegie Bosch Chair。

CMU 是 8 校中转型 AI 路径最丰富但门槛最高的学校：好处是 BSAI minor / additional major 是少数明文允许非 CS 学生拿 AI 学位的设计；坏处是 SCS 选课优先级排他性强，从外院真要选研究生级 AI 课需要项目主任和老师双重通过。最适合的转型路径是 Bio→CompBio MS、Stat→Stat&ML MS、Biz→Tepper Business Analytics（这三条路径在 CMU 内部已经是 ML 主流）。

来源：cs.cmu.edu · stat.cmu.edu/masters · cbd.cmu.edu · tepper.cmu.edu/programs-and-admissions/business-analytics
02

Massachusetts Institute of Technology

麻省理工 · CSAIL（全球最大的 AI 实验室）+ Schwarzman College of Computing 跨校 AI 战略
USNews CS #1
①

AI program 核心专业课 & Listed Faculty

Course 6-4 · MEng · Schwarzman

MIT 的 AI 主干是 Course 6-4: Artificial Intelligence and Decision Making（2022 年开设，本科 + MEng 五年制）。研究生层面通过 EECS MS/MEng 修 AI 方向，同时 Schwarzman College of Computing 推动了 21 个跨学院的"blended majors"（如 6-9 Computation & Cognition、6-7 计算与分子生物、11-6 城市规划+CS）——这是 MIT 与其他校最大的不同。

6.3900Introduction to Machine Learning
6.3950AI, Decision Making, and Society
6.4110Representation, Inference, & Reasoning in AI
6.4120[J]Computational Cognitive Science
6.4200[J]Robotics: Science and Systems
6.4210Robotic Manipulation
6.4300Introduction to Computer Vision
6.4400Computer Graphics
6.5151Large-scale Symbolic Systems
6.5611[J]Quantitative Methods for NLP
6.5831Database Systems
6.5930Hardware Architecture for Deep Learning
6.5940TinyML and Efficient Deep Learning
6.7900Machine Learning
6.7960Deep Learning
6.8200Computational Sensorimotor Learning
6.S898Deep Learning (Special Topics)

Listed Faculty（CSAIL/EECS AI 方向核心，节选）：

Regina Barzilay Tommi Jaakkola Leslie Kaelbling Tomás Lozano-Pérez Aleksander Mądry Antonio Torralba Phillip Isola Stefanie Jegelka Pulkit Agrawal Yoon Kim Jacob Andreas Russ Tedrake
②

非 EECS 系硕士生选 AI 课的政策

Top 8 中最开放
A · 硬性门槛

核心规则：MIT 的 EECS AI 研究生课程（如 6.7900 Machine Learning、6.7960 Deep Learning、6.7920 Reinforcement Learning）不对学生院系做硬性限制——只检查 prerequisites。例如：

6.7960 Deep Learning 的先修：18.05（统计） + (6.3720 / 6.3900 / 6.C01 三选一即可)
6.S898 Deep Learning：(6.3900 或 6.C01 或 6.3720) + (6.3700 或 6.3800 或 18.05) + (18.C06 或 18.06)
6.S977 Diffusion Models：6.7900 + 6.3700 + 18.06 + 18.02

这意味着 MIT 的 Sloan、IDSS、Math、Aero/Astro、HST 的 master 生只要有概率/线代/编程基础即可注册——没有"必须是 EECS 学生"的限制，也没有 reserve seating 给 EECS 优先。少数 IDSS 高级课（如 IDS.190 Topics in Statistics）会标 "Permission of instructor"，但这是普遍学术惯例。

与此同时，MIT 自身 EECS MS 学生每学期最多可在 Harvard / Wellesley 跨注册 1 门 AI 课不付额外学费——这是 MIT 内部的官方 cross-registration 政策。

B · 学位计算

MIT Sloan 的 MBAn（Master of Business Analytics, 1 年）、MFin、SDM（System Design & Mgmt）等 master 都明确允许把 EECS AI 课算入 elective。15-2 Business Analytics + 6-4 双学位已被官方设计为："6.3900 既算 15-2 的 ML requirement，又算 6-4 的 Data-centric requirement"——这种课程双计入两个项目要求的明文 policy，是 8 校中做得最彻底的。

IDSS 的 TPP（Technology & Policy Program）master 可以把 6.7900 / 6.7960 / 6.7800 算入 technical elective。Math / Physics / EAPS 的 master/PhD 学生想做 AI 方向研究，也可以无障碍地修 EECS 研究生课。

来源：deeplearning6-7960.github.io · phillipi.github.io/6.s898 · eecsis.mit.edu/academic-information.html · oge.mit.edu/gpp/admissions-and-registration/cross-registration · mitsloan.mit.edu/programs/undergraduate/double-major-course-15-2-business-analytics-course-6-4
③

与 AI 交叉的硕士项目（6 领域）

MIT × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MIT 无独立 Stat MS
Interdisciplinary Doctoral Program in Statistics (IDPS)
MicroMasters in Statistics & Data Science (online)
Stat 在 IDSS / Math 跨系
Stat Top 5
合并排名
 强
IDSS = ML+Stat+Optim≈ 70%
 强
IDSS 与 CSAIL 大量交叉
课程重合详情
EECS AI 课与 IDS / 18.x Stat 课的重合
全部 重合 elective 独有
课号课程类型
6.7900 / IDS.147Machine Learning（EECS+IDSS 合开）重合
6.7960 / IDS.S97Deep Learning（cross-listed）重合
18.6501 / IDS.013Fundamentals of Statistics重合
6.7820 / IDS.700Statistical Methods in Engineering重合
6.S977Diffusion Modelselective
9.520Statistical Learning Theory（Brain & Cog）elective
18.650Statistics for Applications仅 Math/Stat
6.S898Topics in Deep Learning仅 EECS AI

MIT 的 Stat 课程大量是 EECS / IDSS / Math 三方 cross-listed——重合度本质上是制度化的（同一门课多个编号）。

师资重合详情
同时活跃于 EECS AI 与 IDSS Stat 的教师
全部 joint affiliated
姓名主要方向关系
Philippe RigolletCecil and Ida Green Distinguished Professor of Mathematics + IDSS; ICM 2026 invited speaker (Stat/ML/Image session); statistical optimal transport + mathematical foundations of Transformers; COLT Best Paper; NSF CAREERMath + IDSS, Green Chair, ICM 2026
Ankur MoitraNorbert Wiener Professor of Mathematics + Director, Statistics & Data Science Center (SDSC); 2026 IEEE McDowell Award (high-dim learning, mixture models, robust statistics); ML theoryMath + IDSS, Wiener Chair, SDSC Director
Caroline UhlerHenry L. and Grace Doherty Associate Professor EECS + IDSS; Schmidt Center Co-director at Broad Institute; ML for genomics + causal inference + graphical modelsEECS + IDSS, Doherty Chair
Devavrat ShahAndrew (1956) and Erna Viterbi Professor EECS + IDSS; co-founder Celect (Nike); ML, statistical models, large-scale inferenceEECS + IDSS, Viterbi Chair
Sasha RakhlinDistinguished Professor in Data, Systems and Society + Brain & Cognitive Sciences; statistical learning theory + RL + online learningBCS + IDSS, Distinguished Prof
Tamara BroderickAssociate Professor EECS + LIDS + IDSS; Bayesian inference + uncertainty quantification; teaches ML; NSF CAREEREECS + LIDS + IDSS
Stefanie JegelkaAssociate Professor EECS + IDSS; graph neural networks + ML theory; NSF CAREEREECS + IDSS
Costis DaskalakisArmen Avanessians Professor EECS + IDSS; Nevanlinna Prize 2018 + Simons Investigator; game theory + MLEECS + IDSS, Nevanlinna Prize
Stephen BatesAssistant Professor EECS + IDSS; conformal prediction + statistical ML; uncertainty quantification for AIEECS + IDSS
Guy BreslerAssociate Professor EECS + IDSS; high-dim statistics + algorithmic statistics + graphical modelsEECS + IDSS
Yury PolyanskiyCutten Professor EECS + IDSS; information theory + statistical learning + LLM theoryEECS + IDSS, Cutten Chair
Martin WainwrightCecil & Ida Green Professor EECS + Math + IDSS; high-dim stat + ML; NAS MemberEECS + Math + IDSS, NAS, Green Chair
Ali JadbabaieJR East Professor of Engineering CEE + IDSS; former IDSS Director; network ML + control + RLCEE + IDSS, JR East Chair
Munther DahlehWilliam A. Coolidge Professor EECS + IDSS Founding Director; AI & Data Science Online Program DirectorEECS + IDSS Founding Director
Adit RadhakrishnanAssistant Professor of Mathematics + SDSC; 2025 Edmund F. Kelly Research Award; deep learning theory + neural collapse + kernel MLMath + SDSC
Ashia WilsonLister Brothers Career Development Assistant Professor EECS + IDSS; ML optimization + accelerated methodsEECS + IDSS, Lister Brothers Chair
David GamarnikNanyang Technological University Professor of Operations Research Sloan + IDSS; Erlang Prize; probability + ML on random graphsSloan + IDSS, NTU Chair, Erlang Prize
Rahul MazumderNTU Associate Professor Sloan + IDSS; sparse regression + ML for financeSloan + IDSS, NTU Chair
Youssef MarzoukProfessor Aero/Astro + SDSC; Bayesian inference + ML for physics-based modelingAeroAstro + SDSC
Tommi JaakkolaThomas Siebel Professor EECS + CSAIL + IDSS; generative AI + biology + computational chemistry; ML founding faculty at MITEECS + CSAIL + IDSS, Siebel Chair
Navid AzizanHayes Career Development Professor Mech Eng + IDSS; physics-informed ML + control + LLMMechE + IDSS, Hayes Chair
Math
数学
无独立 Math MS（PhD 直接路径）
18C 应用数学 + 6-4 双专业（本科）
研究生层面 Math 与 EECS 大量交叉
Math MS 几乎不存在
Math #1
USNews
 中
研究生靠 elective 拼凑≈ 50%
 强
Rakhlin / Rigollet 等横跨
课程重合详情
Math 系 与 EECS AI 课程的交叉
全部 重合 elective 独有
课号课程类型
18.065 / 6.C06JMatrix Methods in Data Analysis (Strang)重合
18.408Theoretical Foundations of Deep Learning重合（Math 主开但 EECS 学生选）
6.7900Machine Learningelective
6.7960Deep Learningelective
18.657Topics in High-Dim Statistics & MLelective
18.785Number Theory I仅 Math
6.5151Optimization Methods仅 EECS
师资重合详情
Math 系与 AI 项目共享师资
全部 joint affiliated
姓名主要方向关系
Ankur MoitraNorbert Wiener Professor of Mathematics + SDSC Director; 2026 IEEE McDowell Award; high-dim learning + mixture models + robust statistics + quantum systemsMath + IDSS Director
Philippe RigolletCecil and Ida Green Distinguished Professor of Mathematics; ICM 2026 invited speaker; mathematical foundations of Transformers; statistical optimal transportMath + IDSS, ICM 2026
Adit RadhakrishnanAssistant Professor of Mathematics + SDSC; 2025 Edmund F. Kelly Research Award; deep learning theory + neural collapseMath + SDSC
Elchanan MosselProfessor of Mathematics + SDSC; Simons Investigator + Vannevar Bush Faculty Fellow; probability + Markov chains + ML on networksMath + SDSC
Nike SunProfessor of Mathematics; 2017 Rollo Davidson Prize; combinatorial probability + statistical physics + random CSPs (ML theory adjacent)Math primary
Pablo ParriloJoseph F. and Nancy P. Keithley Professor EECS + Math + LIDS; SIAM Fellow + IEEE Fellow; optimization + ML theoryEECS + Math + LIDS, Keithley Chair
Alan EdelmanProfessor of Applied Mathematics; creator of Julia language; numerical linear algebra + ML at scale + automatic differentiationMath primary, Julia Lang
Jonathan KelnerProfessor of Applied Mathematics + CSAIL; algorithm design + ML optimization + spectral methodsMath + CSAIL
Martin WainwrightCecil & Ida Green Professor Math + EECS + IDSS; NAS Member; high-dim statistics + ML; "High-Dimensional Statistics" textbookMath + EECS + IDSS, NAS
Bonnie BergerSimons Professor of Mathematics + CSAIL Computation & Biology Group Head; AAAS + ACM + AMS + ISCB Fellow; LLM for biological sequences + ML for structural biologyMath + CSAIL, Simons Chair, multi-Fellow
Bio
生物
M.Eng. in 6-7（CS + Molecular Biology, 5 年制本硕连读）
HST PhD in Medical Engineering / Bioinformatics
CSAIL Jameel Clinic（ML for Health 研究项目）
6-7 是 blended major 制度化
Bio Top 1
USNews
 强
6-7 课表直接 CS+Bio≈ 80%
 强
Barzilay / Berger 跨
课程重合详情
6-7 / HST 与 EECS AI 的课程互通
全部 重合 elective 独有
课号课程类型
6.8701 / 7.91Computational Biology（cross-listed）重合
6.8711 / HST.507Computational Systems Biology重合
6.7930 / HST.956Machine Learning for Healthcare重合
6.7900Machine Learning（6-7 必修核心）elective
7.36Computational & Systems Biologyelective
7.06Cell Biology仅 Bio
HST.583Functional MRI Analysis仅 HST

MIT 不设独立 Statistics Department, 而是通过 Statistics & Data Science Center (SDSC)（统计研究中心, 隶属 IDSS）+ Institute for Data, Systems, and Society (IDSS)（数据/系统/社会研究所）+ LIDS (Laboratory for Information and Decision Systems) 三层架构组织统计师资。SDSC 由 Ankur Moitra (Wiener Chair, 2026 IEEE McDowell Award) 任主任。Interdisciplinary Doctoral Program in Statistics (IDPS) 是 MIT 唯一的统计博士项目。MicroMasters in Statistics & Data Science（IDSS 主办）+ MITx Online "AI & Data Science Certificate" 是 master-prep 项目。

师资重合详情
EECS / Bio / HST / Jameel Clinic 跨界教师
全部 joint affiliated
姓名主要方向关系
Bonnie BergerSimons Professor of Mathematics + CSAIL Computation & Biology Group Head; AAAS + ACM + AMS + ISCB + AMS Fellow; LLM for biological sequences + ML for structural biology + compressive genomicsMath + CSAIL, Simons Chair
Manolis KellisProfessor EECS + CSAIL + Broad Institute; ML for regulatory genomics + epigenomics + Alzheimer's genetics; ENCODE/Roadmap Epigenomics PIEECS + CSAIL + Broad
Tommi JaakkolaThomas Siebel Professor EECS + CSAIL + IDSS; generative AI + ML for biology + computational chemistry; protein structure predictionEECS + CSAIL + IDSS, Siebel Chair
Caroline UhlerDoherty Associate Professor EECS + IDSS + Schmidt Center Co-director at Broad Institute; AI-driven multi-omics + causal inference + cell-state representation learningEECS + IDSS + Broad, Doherty Chair
Christopher BurgeProfessor of Biology + CSB program; computational and ML approaches to RNA splicing regulationBiology primary
Aviv RegevProfessor of Biology (on leave) + Broad Institute Core Member; now Genentech EVP Research; single-cell genomics + ML for cellular states; HHMI InvestigatorBio + Broad, HHMI
Ernest FraenkelProfessor of Biological Engineering + CSB; ML for cancer systems biology + precision medicine + multi-omic networksBE primary
Eric AlmProfessor of Biological Engineering; ML for microbiome + metagenomic networksBE primary
David GiffordProfessor of EECS + CSB faculty; ML for genomics + clinical data + immunoengineering; CSAILEECS + CSB + CSAIL
Pulin LiAssistant Professor of Biology + Whitehead Institute; ML for gene regulation + tissue patterningBiology primary
James CollinsTermeer Professor of Medical Engineering and Science + CSB + Broad Institute; ML-driven drug discovery (halicin antibiotic with AI); NAS + NAE MemberMES + Broad, NAS+NAE
Pardis SabetiProfessor of Organismic and Evolutionary Biology (Harvard) + Broad Member + CSB faculty; ML for genomics + epidemic surveillance; HHMI InvestigatorBroad + CSB, HHMI
Chem
化学
无 AI×Chem 专门 master
Chemistry MS 极少（PhD 路径为主）
Chemical Engineering（10）+ EECS 双方向
Schwarzman 资助"AI in Chem"研究
Chem Top 1–2
 中
需自行设计≈ 35%
 中
Coley / Jensen 等跨界
课程重合详情
Chem/ChemE 中含 ML 元素的课程
全部 重合 elective 独有
课号课程类型
10.34 / 10.637ML for Molecular Engineering（Coley）重合
3.022Microstructural Evolution（含 ML 模块）重合
6.7900Machine Learningelective
6.S977Diffusion Models（用于分子生成）elective
5.07Biological Chemistry仅 Chem
10.37Chemical Reactor Engineering仅 ChemE
师资重合详情
化学 / 化工 系做 ML 的教师
全部 joint affiliated
姓名主要方向关系
Heather KulikProfessor of Chemical Engineering + Chemistry; ML-augmented quantum chemistry + transition metal catalysts + autonomous chemistry; NSF CAREER + Sloan FellowChemE + Chem
Adam WillardFrancis Wright Davis Professor (effective 2025-07) of Chemistry; ML for liquid water + electrochemistry + statistical mechanics of disorderChem primary, Davis Chair
Rafael Gómez-BombarelliAssociate Professor of Materials Science and Engineering (DMSE); physics-based atomistic simulation + ML for materials discovery; co-founder Calculario; pioneered VAE-based molecular generative modelDMSE primary
Connor ColeyClass of 1957 Career Development Assistant Professor of Chemical Engineering + EECS; ML for synthesis planning + molecular generation + autonomous chemistry; teaches ML for chemistryChemE + EECS
Klavs JensenWarren K. Lewis Professor of Chemical Engineering + Materials Science; NAE Member; ML-guided continuous flow chemistry + autonomous synthesisChemE + DMSE, NAE
William GreenProfessor of Chemical Engineering; ML-augmented combustion kinetics + reaction mechanism generation (RMG)ChemE primary
Markus BuehlerJerry McAfee Professor of Engineering CEE + DMSE; protein materiomics + LLM for materials; Foresight Feynman PrizeCEE + DMSE, McAfee Chair
Tess SmidtAssistant Professor of EECS; 2025 Schmidt Sciences AI2050 Early Career Fellowship; equivariant neural networks for chemistry + materials (e3nn)EECS primary
Phys
物理
无 Physics MS（PhD 路径）
IAIFI（NSF AI Institute for AI & Fundamental Interactions, MIT-led）
Schwarzman College 跨学科
研究项目而非教学项目
Phys Top 1
USNews
 中
研究层面强≈ 40%
 强
Tegmark/Williams/Thaler 跨
课程重合详情
Physics 系含 AI/ML 的课程
全部 重合 elective 独有
课号课程类型
8.S50Modern Physics & AI（IAIFI 系列）重合
8.S20Physics Applications of ML重合
6.7900Machine Learningelective
6.7960Deep Learningelective
8.371Quantum Information Science Ielective
8.04Quantum Physics I仅 Phys
8.962General Relativity仅 Phys
师资重合详情
Physics × ML 跨界教师（IAIFI 是核心载体）
全部 joint affiliated
姓名主要方向关系
Jesse ThalerProfessor of Physics + IAIFI Director; ML for high-energy physics + symmetry-aware ML; LNSPhysics + IAIFI Director
Mike WilliamsProfessor of Physics + IAIFI Deputy Director; ML for LHCb experiment + physics-of-grokking representation learning; fastmachinelearning.org co-leadPhysics + IAIFI Deputy
Max TegmarkProfessor of Physics + MIT Kavli Institute; Future of Life Institute President; physics-inspired ML + AI safety + KAN networksPhysics + Kavli, FLI
Phiala ShanahanClass of 1957 Career Development Associate Professor of Physics + LNS; ML for theoretical nuclear physics (lattice QCD) + DeepMind collaboration; 2020 Kenneth G. Wilson AwardPhysics + LNS, IAIFI
Marin SoljačićCecil and Ida Green Professor of Physics; MacArthur Fellow; ML for photonics + nanostructure designPhysics + IAIFI, Green Chair
Isaac ChuangJulius A. Stratton Professor in EE and Physics; quantum computing + ML; MIT IAIFI senior investigatorPhysics + EECS + IAIFI, Stratton Chair
Tracy SlatyerJerrold Zacharias Professor of Physics; 2024 Guggenheim Fellow + 2021 New Horizons in Physics Prize; ML for dark matter searchesPhysics + IAIFI, Zacharias Chair
Phil HarrisClass of 1958 Career Development Associate Professor of Physics; fast ML for particle physics; fastmachinelearning.org + a3d3.ai co-leadsPhysics + IAIFI
Pulkit AgrawalSteven and Renee Finn Career Development Assistant Professor of EECS + IAIFI; RL + robot learning + physics-informed AIEECS + IAIFI
Tess SmidtAssistant Professor of EECS; 2025 Schmidt Sciences AI2050 Early Career Fellowship + IAIFI Senior Investigator; equivariant neural networks for physicsEECS + IAIFI
Bill FreemanThomas and Gerd Perkins Professor of EECS + IAIFI senior investigator; NAE Member (computer vision contributions); ML for image processingEECS + IAIFI, NAE, Perkins Chair
William DetmoldProfessor of Physics + LNS; lattice QCD + ML for theoretical nuclear physics; IAIFI senior investigatorPhysics + IAIFI
Biz
商科
MIT Sloan MBAn (Master of Business Analytics, 12 个月)
MFin (Master of Finance)
15-2 双学位本科（已制度化双计 6-4）
SDM (System Design & Management)
MBAn 即 ML 主导
Biz Analytics #1
QS
 强
核心课即 ML≈ 70%
 强
Sloan + IDSS + EECS 共聘
课程重合详情
Sloan MBAn / MFin 中的 ML 课程
全部 重合 elective 独有
课号课程类型
15.072Advanced Analytics Edge（MBAn 必修）重合
15.095ML Under Modern Optimization Lens重合（Bertsimas）
15.093 / 6.255 / IDS.200Optimization Methods（三方 cross-listed）重合
15.S08Hands-On Deep Learning重合（Sloan 新课）
15.457Advanced Analytics of Finance（MFin）重合
6.7900Machine Learning（EECS 主开，Sloan 学生可选）elective
15.481Modeling with ML: Financial Technologyelective（MFin Concentration）
15.097AI & Machine Learning Research in Financeelective
15.401Finance Theory I（MFin 必修）仅 MFin
15.681From Analytics to Action（MBAn 必修）仅 MBAn

MIT Mathematics（Course 18, 约 50 教授）通过 Applied Math 群体（Edelman/Parrilo/Berger/Strang/Goemans）+ Statistical/Probability 群体（Rigollet/Moitra/Mossel/Sun）+ SDSC 联合席位与 ML 形成密集桥梁。Edelman 是 Julia 编程语言创始人（科学计算+ML 主流语言之一）。Berger 任 CSAIL Computation & Biology Group Head, 横跨 Math/CSAIL/Bio 三系。Math 系毕业生进 AI master 主走 IDSS-IDPS PhD 通道, 也可选 SDSC affiliated MS。

师资重合详情
Sloan / EECS / IDSS 跨界教师
全部 joint affiliated
姓名主要方向关系
Dimitris BertsimasBoeing Leaders for Global Operations Professor + MIT Vice Provost for Open Learning + Associate Dean Online Education and AI; founded MBAn 2013 (#1 globally since inception); INFORMS Edelman Laureate + von Neumann Theory Prize 2019; ML/optimization for healthcareSloan OR primary, Boeing Chair, MBAn Founder
Retsef LeviJ. Spencer Standish (1945) Professor of Operations Management; NLP for supply chain + LLM for operations; food safety NLPSloan OM, Standish Chair
Georgia PerakisWilliam F. Pounds Professor of Management Science + Associate Dean Sloan; INFORMS Fellow; ML for revenue management + price optimizationSloan OR, Pounds Chair, INFORMS Fellow
David GamarnikNanyang Technological University Professor of Operations Research + IDSS core; Erlang Prize; ML on random graphs + statistical physics methodsSloan OR + IDSS, NTU Chair
Rahul MazumderNanyang Technological University Associate Professor Sloan + IDSS; sparse regression + interpretable ML + optimization for MLSloan OR + IDSS
Negin GolrezaeiTheresa Seley (1955) Associate Professor in Management Science; online learning + auctions + bandit RL; INFORMS Pierskalla AwardSloan OM, Seley Chair
Vivek FariasPatrick J. McGovern (1959) Professor; demand forecasting ML + operations research; INFORMS Frederick W. Lanchester PrizeSloan OR, McGovern Chair
Stephen GravesAbraham J. Siegel Professor of Management Science; NAE Member; supply chain ML + production planningSloan OM, Siegel Chair, NAE
Alexandre JacquillatAssociate Professor of Operations Research and Statistics; ML + optimization for transportation + airline operationsSloan OR primary
Sinan AralDavid Austin Professor of Management + IDE Director; IDSS member; large-scale ML for social networks + computational social science; "The Hype Machine" authorSloan + IDSS, Austin Chair
Andrew LoCharles E. and Susan T. Harris Professor + Director MIT Lab for Financial Engineering; NBER + AAAS Fellow; quantitative finance + ML for biotech investmentSloan Finance, Harris Chair

MIT 与 CMU 路径不同：MIT 没有独立的"AI 硕士"，而是通过 EECS MEng/MS 加上跨学院的 blended majors 实现。这意味着对于已经在 MIT 内部其他系的学生，转型 AI 几乎没有制度门槛，但对外申请来说更难——你不能"申请一个 AI MS"，要么走 6-4 MEng（需 MIT 本科背景），要么 EECS MS（极难），要么 Sloan MBAn（商学院路径）。最现实的转型入口是 Sloan MBAn（商→AI）和 6-7 / IDSS PhD pipeline（理工科→AI）。

来源：catalog.mit.edu · idss.mit.edu · mitsloan.mit.edu/mban · 6.eecs.mit.edu
03

Stanford University

斯坦福 · HAI（Human-Centered AI Institute）+ CS 系 AI Specialization 是研究生 AI 教育的范本
USNews CS #1
①

AI program 核心专业课 & Listed Faculty

MS CS · AI Specialization

Stanford 把 AI 作为 MS CS 的 9 个 specializations 之一（最受欢迎）。结构：必修 CS 221 + 至少 4 门"Track B"深度课 + 总共 21 unit。Track B 是 AI 最核心的一组课。

CS 221AI: Principles & Techniques（必修）
CS 223AIntroduction to Robotics
CS 224NNLP with Deep Learning
CS 224SSpoken Language Processing
CS 224UNatural Language Understanding
CS 224VConversational Virtual Assistants with DL
CS 224WMachine Learning with Graphs
CS 228Probabilistic Graphical Models
CS 229Machine Learning
CS 229MMachine Learning Theory
CS 230Deep Learning
CS 231AComputer Vision: 3D Reconstruction
CS 231NCNN for Visual Recognition
CS 234Reinforcement Learning
CS 236Deep Generative Models
CS 237A/BPrinciples of Robot Autonomy
CS 238Decision Making under Uncertainty
CS 273A/BComputational Genomics
CS 279Computational Biology
CS 329SML Systems Design

Listed Faculty（CS AI Group + HAI 核心，节选）：

Fei-Fei Li Andrew Ng Christopher Manning Percy Liang Stefano Ermon Chelsea Finn Dorsa Sadigh Tatsunori Hashimoto Jure Leskovec Emma Brunskill Jiajun Wu Carlos Guestrin
②

非 CS 系硕士生选 AI 课的政策

无院系限制 · 但有抢课压力
A · 硬性门槛

核心规则：Stanford 没有"必须是 CS 学生"的硬性 enrollment 限制——任何在读 master 都可以注册 CS 研究生课，只要 prerequisites 满足。CS 229 / CS 230 / CS 231N 等热门课的 prereqs 都是线性代数 + 概率统计 + Python 编程，这是 Stat / ICME / MS&E / EE / BMI 等 master 项目学生天然具备的。

但有两层"软性"门槛：

(1) Enrollment Group 优先级：Stanford 的注册系统按"已完成学期数"分组排序——second-year 优先于 first-year。CS MS 自己的学生（一般两年项目）在选 CS 课时和外系 master 学生处于同一池子，但热门课会启用 "reserve seating"——为特定项目预留座位。CS 229、230、224N、231N 等 200-level AI 主干课都属于这种情况。
(2) Permission number：少数高级 / 容量极小的课会要求 instructor 给 permission number（如 CS 379C 这样的 seminar）。常规 200-level AI 课基本不需要。

实例：Stat MS 的 program sheet 已经把 CS 229、CS 224N、CS 224W 列入认可的 ML elective；ICME MS 的 Data Science Track 把 CS 229 列为可选 depth course。这种课表互认是制度性的。

B · 学位计算

Stanford CS MS 自己的规定："Non-CS courses must be technical courses numbered above 100, related to the degree program, and approved by the adviser"——反过来同样适用于其他系。Stat / MS&E / ICME / BMI 的 master sheet 都明确列出 CS 229 / 230 / 224N 等可作为 elective。

这种"双向开放"是 Stanford 的特色——任何 Stanford master 都可以以 elective 形式纳入 CS AI 课，且不需要特别申请；只是抢课难。

来源：cs.stanford.edu/masters-specializations · cs.stanford.edu/masters-frequently-asked-questions · studentservices.stanford.edu/my-academics/enroll-classes/when-do-i-enroll/enrollment-group-frequently-asked-questions
③

与 AI 交叉的硕士项目（6 领域）

Stanford × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MS in Statistics
MS in Statistics – Data Science Track
(Stanford 不再为 PhD 生颁 Stat MS)
Stat 系直接管理
Stat #1
USNews tied #1
 强
CS 229 直接列入 Stat MS≈ 75%
 强
Stat ↔ CS 长期联合
课程重合详情
CS AI 课与 Stat MS 的明文互认
全部 重合 等价 elective 独有
课号课程类型
CS 229Machine Learning重合（Stat MS sheet）
CS 230Deep Learning重合
CS 224WML on Graphs重合
STATS 315AModern Applied Statistics: Learning重合（即 ESL 教材）
STATS 315BModern Applied Stats: Data Mining重合
STATS 305A/B/CTheory of Statistics等价（CS PhD 也常选）
CS 228Probabilistic Graphical Modelselective
CS 234Reinforcement Learningelective
STATS 200Introduction to Statistical Inference仅 Stat
STATS 270Bayesian Inference仅 Stat

MIT 不设独立 master in Computational Biology, 但 Computational and Systems Biology (CSB) PhD Program 是 master-prep 通道。Course 7 (Biology) + Course 20 (Biological Engineering) + CSAIL Computation & Biology Group + Broad Institute 形成 MIT-Bio AI 紧密网络。Bonnie Berger 任 CSAIL Comp & Bio Group Head, 是该网络的核心 PI。Aviv Regev 已离任 MIT 转 Genentech EVP Research（仍保留 on-leave Bio Faculty 头衔）。Caroline Uhler 任 Broad Institute Schmidt Center Co-director, 是 ML × 生物的旗舰角色。

师资重合详情
同时活跃于 Stat MS 与 CS AI 的教师
全部 joint affiliated
姓名主要方向关系
Andrea MontanariDepartment Chair + Professor of Statistics; high-dim statistics + ML + probability theory; previously EE → Stat; ICM 2022 invited speakerStat Chair
Emmanuel J. CandèsBarnum-Simons Chair in Math and Statistics + Professor of Stat & Math; compressed sensing + high-dim stats + signal processing; NAS Member + AAA&S; co-founder C4DUStat + Math, Barnum-Simons Chair, NAS
David DonohoAnne T. and Robert M. Bass Professor in H&S; signal processing + deep learning + compressed sensing + harmonic analysis; NAS Member + Gauss Prize 2018Stat + ICME, Bass Chair, NAS, Gauss Prize
Trevor J. HastieJohn A. Overdeck Professor + Professor Emeritus Stat + Biomedical Data Science; statistical learning + ML; "ESL" + "ISL" textbook; co-author with Tibshirani & FriedmanStat + DBDS, Overdeck Chair
Robert TibshiraniProfessor of Stat + Biomedical Data Science; lasso + statistical learning textbook author; NAS Member; ISL textbook with Hastie, James, WittenStat + DBDS, NAS
Jerome H. FriedmanProfessor Emeritus; gradient boosting (GBM/MART) + ML/data mining pioneer; "ESL" textbook co-authorStat Emeritus
Emily B. FoxProfessor of Statistics + Computer Science + ICME; ML for health, time series, computational neuroscience; previously Apple Distinguished Engineer (Health AI)Stat + CS + ICME
Wing Hung WongProfessor of Stat + Health Research and Policy; computational biology + statistical inference + ML; NAS MemberStat, NAS
John DuchiAssociate Professor of Stat + EE; ML + information theory + stochastic optimization; AdaGrad co-creator (with Hazan, Singer); NSF CAREERStat + EE
Scott LindermanAssistant Professor of Stat + Wu Tsai Neuro affiliated; probabilistic ML + computational neuroscienceStat + Wu Tsai Neuro
Susan HolmesProfessor Emerita; computational statistics + bootstrap + multivariate analysis + data mining (microbiome ML)Stat Emerita
Bradley EfronMax H. Stein Professor + Professor Emeritus Stat; National Medal of Science 2005 + bootstrap method; Bayesian + biostatisticsStat Emeritus, National Medal of Science
Iain JohnstoneProfessor of Statistics; signal processing + asymptotic theory of high-dim estimation; NAS Member + AAA&SStat, NAS
Persi DiaconisMary V. Sunseri Professor of Stat & Math; Bayesian + statistical computing + MCMC; NAS Member + MacArthur FellowStat + Math, NAS, MacArthur
Brian TrippeAssistant Professor of Stat; probabilistic ML + Bayesian computation + protein engineeringStat primary
Tijana ZrnicAssistant Professor of Stat; performative prediction + prediction-powered inference; ML safety + active inferenceStat primary
Tselil SchrammAssistant Professor of Stat; algorithms + ML + computational statistics + high-dim statsStat primary
Dominik RothenhäuslerAssistant Professor of Stat; causal inference + distributional robustness + high-dim stats + graphical modelsStat primary
Stefan WagerCourtesy Associate Professor of Stat + Associate Professor of OIT (GSB); causal inference + ML; co-creator generalized random forestsStat + GSB OIT
Percy LiangCourtesy Associate Professor of Stat + Associate Professor of CS; NLP + LLM (HELM, Codalab); SAIL + CRFM (Center for Research on Foundation Models) Director; HAI Senior FellowStat + CS, CRFM Director
Lester MackeyAdjunct Professor of Stat + Microsoft Research Senior Principal Researcher; high-dim stats + ML + algorithms; SLAC HEP ML co-authorStat + MSR
Lihua LeiCourtesy Assistant Professor of Stat + Assistant Professor at GSB OIT; causal inference + high-dim stats + multiple testing + MLStat + GSB OIT
Julia SalzmanCourtesy Associate Professor of Stat + Associate Professor of Biomedical Data Science + Biochem; deep learning for genomics + bioinformaticsStat + DBDS + Biochem
Chiara SabattiProfessor of Stat + Biomedical Data Science; Bayesian + computational biology + statistical geneticsStat + DBDS
Julia PalaciosAssociate Professor of Stat; graphical models + Bayesian + evolutionary genomicsStat primary
Balasubramanian NarasimhanSenior Research Scientist Stat + DBDS; ML + bioinformatics + statistical computing + clinical trialsStat + DBDS
Hua TangCourtesy Professor of Stat + Professor of Genetics; statistical genetics + computational biology + population genomicsStat + Genetics
Stephen BoydSamsung Professor in Engineering + Professor of EE + ICME; convex optimization for ML + control + finance; NAE Member + AAA&S; "Convex Optimization" textbookEE + ICME, NAE, Samsung Chair
Math
数学
MS in Mathematics（无 AI 专门 track）
MS in Computational & Mathematical Engineering (ICME) — 实际是数学背景转 AI 的最佳入口
ICME 为转型而设计
Math Top 5
USNews
 强
ICME 课表覆盖 ML/Optim≈ 70%
 强
ICME 与 CS、Stat 联合
课程重合详情
Math/ICME 与 CS AI 课的交叉
全部 重合 elective 独有
课号课程类型
CME 250Intro to Machine Learning（ICME 必修选项）重合
CME 305Discrete Mathematics & Algorithms重合（与 CS 161 等价）
CME 307 / MS&E 311Optimization重合
CS 229Machine Learning（ICME 学生可计 elective）elective
CS 230Deep Learningelective
CME 200Linear Algebra with Applicationselective
MATH 230Probability Theory仅 Math
MATH 285Topics in Probability仅 Math

MIT 化学 ML 横跨四个系: Chemistry (Course 5) + Chemical Engineering (Course 10) + Materials Science (Course 3) + EECS。化学背景学生进 AI master 实际无独立 chem AI master, 走 Chemistry PhD 选 ML PI（Kulik/Willard）+ MIT-IBM Watson AI Lab合作，或申请 ChemE / DMSE PhD（Coley/Gómez-Bombarelli/Buehler）。Tess Smidt 在 EECS 但研究 ML for chemistry/materials (e3nn 库)。

师资重合详情
Math 系或 ICME 中做 ML 研究的教师
全部 joint affiliated
姓名主要方向关系
Emmanuel J. CandèsBarnum-Simons Chair in Math and Statistics; compressed sensing + high-dim statistics; NAS Member; co-founder C4DUMath + Stat, NAS
Persi DiaconisMary V. Sunseri Professor of Stat & Math; Bayesian + MCMC + combinatorics; NAS Member + MacArthur FellowMath + Stat, NAS, MacArthur
Sourav ChatterjeeProfessor of Math & Stat; probability theory + statistical physics methods (relevant to deep learning theory)Math + Stat
Amir DemboMarjorie Mhoon Fair Professor in Quantitative Science + Professor of Math & Stat; large deviations + probability; ML theoryMath + Stat, Fair Chair
George PapanicolaouRobert Grimmett Professor in Math, Emeritus; applied math + stochastic methods + ML for finance/imagingMath Emeritus, Grimmett Chair
Moses CharikarDonald E. Knuth Professor of CS + courtesy in Math; theory of ML + algorithms + sublinear algorithmsCS + Math, Knuth Chair
Bio
生物
MS in Biomedical Informatics (BMI)
MS in Computational & Mathematical Engineering – Biocomputation Track
MS in Health Policy / Statistics（次要）
BMI 是顶级 AI×Bio 项目
Med #4
USNews
 强
BMI 必修就是 ML≈ 75%
 强
BMI 主任跨 CS
课程重合详情
BMI / Biocomputation 与 CS AI 课的重合
全部 重合 elective 独有
课号课程类型
BIOMEDIN 215Data-Driven Medicine重合
BIOMEDIN 217Translational Bioinformatics重合
BIOMEDIN 260Computational Methods for Biomedical Image Analysis重合
CS 273AGenomics重合（与 BIOMEDIN 273 cross-listed）
CS 273BDeep Learning for Genomics重合（明文 cross-listed BIOMEDIN 273B）
CS 229Machine Learning（BMI 必选 ML 之一）elective
CS 230Deep Learningelective
BIOMEDIN 211Genomics仅 BMI
BIOMEDIN 212Representations & Algorithms in Comp Bio仅 BMI

MIT 物理 AI 主体在 NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI, 2020 立项, MIT 牵头, 跨 MIT/Harvard/Northeastern/Tufts), MIT 一家就有 16 位 IAIFI Senior Investigators (LNS/RLE/Kavli/CSAIL/IDSS)。Jesse Thaler 任 IAIFI Director, Mike Williams 任 Deputy Director。MIT 有专门的 Interdisciplinary PhD Program in Physics, Statistics, and Data Science (PhysSDS)（由 Thaler/Williams/Rakhlin 联合发起）, 物理生进 AI 直接走 PhysSDS。Course 8.S50 "Physics in Data Science" + MITx 在线课已成形。

师资重合详情
BMI 与 CSAIL/HAI 共聘师资
全部 joint primary affiliated
姓名主要方向关系
James ZouAssociate Professor of Biomedical Data Science + CS + EE; 2025 ISCB Overton Prize; deep learning for genomics + drug discovery + foundation models for biomedicineDBDS + CS + EE, ISCB Overton 2025
Anshul KundajeAssociate Professor of Genetics + Computer Science; deep learning for regulatory genomics; teaches CS 273B "Deep Learning in Genomics and Biomedicine"Genetics + CS
Robert TibshiraniProfessor of Biomedical Data Science + Statistics; NAS Member; lasso + statistical learning + ML for clinical genomicsDBDS + Stat, NAS
Trevor HastieJohn A. Overdeck Professor Stat + Biomedical Data Science Emeritus; statistical learning + MLStat + DBDS Emeritus
Chiara SabattiProfessor of Biomedical Data Science + Stat + Genetics; Bayesian + computational biology + statistical geneticsDBDS + Stat + Genetics
Mert PilanciAssistant Professor of EE + Biomedical Data Science by courtesy; convex optimization + neural network theory + ML compressionEE + DBDS by courtesy
Russ AltmanKenneth Fong Professor + Professor of Bioengineering, Genetics, Medicine, BMI; NAS + NAM (National Academy of Medicine) Member; former DBDS chair; AI for drug discovery + PharmGKBBME + Genetics + DBDS, Fong Chair, NAS+NAM
Michael SnyderStanford W. Ascherman Professor + Genetics Chair; multi-omics ML + personalized medicine; co-founder of Personalis, SensOmicsGenetics Chair, Ascherman Chair
Carlos BustamanteProfessor of Biomedical Data Science (on leave) + CEO Galatea Bio; NAM Member; ML for population genomicsDBDS, NAM
Manish SaggarAssistant Professor of Psychiatry + Biomedical Data Science (by courtesy); ML for neuroimaging + dynamic brain networksPsych + DBDS
Olivier GevaertAssociate Professor of Biomedical Informatics Research + Biomedical Data Science; ML for cancer multi-omics + integrative omicsDBDS primary
Jonathan PritchardProfessor of Genetics + Biology; NAS Member + HHMI Investigator; population genetics + ML for genomicsGenetics + Bio, NAS, HHMI
Chem
化学
无独立 AI×Chem master
MS in Chemistry 极少，主要 PhD 路径
MS in Materials Science & Engineering（MSE）含 ML for Materials
Chem MS 不发达
Chem Top 3
USNews
 中
仅 MSE 路径明确≈ 35%
 中
Pande / Reisman 等
课程重合详情
Chemistry/MSE master 中可触及的 AI/ML 课
全部 重合 elective 独有
课号课程类型
MATSCI 215Computational Materials Science重合（含 ML 模块）
CHEM 274Molecular Mechanics & Dynamics重合（与 ML 应用紧密）
CS 229Machine Learningelective
CS 273BDeep Learning for Genomics（也用于 Chem）elective
CHEM 273Computational Chemistry仅 Chem
CHEM 175Inorganic Chemistry仅 Chem

MIT Sloan Master of Business Analytics (MBAn)由 Bertsimas 2013 年创立, 入学约 80 人/届, 12 个月, 与 MIT Operations Research Center (ORC)联合, USNews Business Analytics 排名 #1（自创立以来）。课程包括 15.095 "Machine Learning Under a Modern Optimization Lens"（Bertsimas）+ Optimization Methods + Capstone。Bertsimas 在 2025 年成为 MIT Vice Provost for Open Learning + Associate Dean of Online Education and AI。Sloan IDE (Initiative on the Digital Economy)由 Sinan Aral 主导。MIT Lab for Financial Engineering (LFE)由 Andrew Lo 领导。

师资重合详情
化学/材料 × ML 跨界教师
全部 joint affiliated
姓名主要方向关系
Todd MartínezDavid Mulvane Ehrsam and Edward Curtis Franklin Professor of Chemistry + SLAC; quantum chemistry ML + neural network potentials + ab initio molecular dynamics; NAS MemberChem + SLAC, Ehrsam-Franklin Chair, NAS
Thomas MarklandProfessor of Chemistry; neural network potentials + machine learning for chemistry (TorchMD-Net 2.0, SPICE biomolecular ML)Chem primary
Grant RotskoffAssistant Professor of Chemistry; ML for chemistry + statistical physics of deep learning + accelerated MD samplingChem primary
Carolyn BertozziAnne T. and Robert M. Bass Professor of Chemistry, Baker Family Director ChEM-H, HHMI Investigator; 2022 Nobel Prize in Chemistry; computational chemistry + ML for glycoscienceChem + ChEM-H, Nobel 2022, HHMI
Fang LiuAssistant Professor of Chemistry; computational quantum chemistry + ML interatomic potentials + reaction discoveryChem primary
Steven BoxerCamille Dreyfus Professor of Chemistry; NAS Member; ML for vibrational spectroscopy (with Markland); biophysical chemChem, Dreyfus Chair, NAS
Vijay PandeHenry Dreyfus Professor of Chemistry, Emeritus + Andreessen Horowitz General Partner (Bio fund); Folding@home founder; ML for drug discovery + quantum chemistryChem Emeritus, a16z Bio
Phys
物理
无 Physics MS（PhD 路径）
SLAC + Stanford 跨学科研究
Applied Physics MS（应用方向，可走 ML）
物理 master 罕见
Phys Top 3
USNews
 中
需自行设计≈ 40%
 中
仅个别交叉
课程重合详情
物理 master 触及的 AI/ML 课
全部 重合 elective 独有
课号课程类型
PHYSICS 290Special Topics: ML for Physics重合
STATS 315AModern Applied Stats: Learning重合（物理常选）
CS 229Machine Learningelective
CS 234Reinforcement Learningelective
PHYSICS 110Advanced Mechanics仅 Phys
PHYSICS 230Quantum Mechanics仅 Phys

Stanford Statistics Department (in School of H&S, Sequoia Hall) 设 MS in Statistics + MS in Statistics: Data Science Specialization（与 ICME / CS 联合）。Stat 系研究关键词页明确列出 machine learning, deep learning, NLP, statistical learning, probabilistic ML, causal inference, performative prediction, prediction-powered inference, high-dim stats 等 AI/ML 关键词。Stat 系还与 Department of Biomedical Data Science (DBDS)（Stanford Medicine）+ ICME（工程院）+ Stanford Data Science (SDS)（跨校区）+ Center for Decoding the Universe (C4DU)（与 KIPAC 联合, Candès 是创始团队）形成网状架构。

师资重合详情
Physics × ML 跨界教师
全部 joint affiliated
姓名主要方向关系
Risa WechslerDirector, KIPAC + Humanities and Sciences Professor of Physics + Particle Physics & Astrophysics + Director Center for Decoding the Universe; NAS + AAA&S + APS Fellow + AAAS Fellow; Associate Director Stanford Data Science; ML for cosmology (Dark Energy Survey, DESI, Rubin/LSST)Physics + KIPAC + SDS, NAS, KIPAC Director
Susan ClarkAssistant Professor of Physics + C4DU Co-director; ML for galactic interstellar medium + cosmologyPhysics + C4DU
Tom AbelProfessor of Physics + KIPAC + SLAC; cosmological N-body simulations + ML emulatorsPhysics + KIPAC + SLAC
Phil MarshallKIPAC + SLAC Senior Scientist; ML for Rubin Observatory LSST + strong lensing detectionKIPAC + SLAC
Peter GrahamProfessor of Physics; theoretical particle/astro physics, ML applications in dark matter searchesPhysics primary
Ching-Yao LaiAssistant Professor of Geophysics + ICME affiliated; physics-informed neural networks + ML for ice dynamics + climate physicsGeophysics + ICME
Lester MackeyAdjunct Professor of Stat + MSR Senior Principal; SLAC ML co-author (Jet Images deep learning, particle physics ML)Stat + MSR, SLAC ML
Stefano ErmonAssociate Professor of Computer Science + HAI; generative ML + ML for sustainability/physical sciences; SLAC FEL Bayesian opt collaboratorCS + HAI, SLAC ML
Daniel RatnerLead, ML Initiative at SLAC + LCLS Accelerator Physics; Bayesian optimization for FEL + accelerator MLSLAC primary
Biz
商科
MS in Management Science & Engineering (MS&E)
GSB MBA + 1 年加修 CS／MS&E
Stanford GSB 没有专门的 MSBA
MS&E 是 Stanford 商科入 AI 的核心
GSB MBA #1
USNews
 强
MS&E 含 ML 主线≈ 65%
 强
MS&E 与 CS 联合教席
课程重合详情
MS&E / GSB 中的 AI 课程
全部 重合 elective 独有
课号课程类型
MS&E 226Fundamentals of Data Science重合（含 ML 基础）
MS&E 235Reinforcement Learning（应用方向）重合
MS&E 252Decision Analysis I重合
CS 229Machine Learningelective
CS 230Deep Learningelective
CS 234Reinforcement Learningelective
MS&E 211Linear & Nonlinear Optimizationelective
GSB FIN 380Empirical Asset Pricing仅 GSB
GSB MGT 248Strategy Beyond Markets仅 GSB

Stanford Math 系（Building 380, ~50 教授）以纯数学为主, 与 ML 桥梁主要通过 (1) Candès 同时持 Math + Stat 双系 endowed chair（Barnum-Simons）; (2) 概率方向 Chatterjee/Dembo 与 ML 理论交叉; (3) ICME (Institute for Computational and Mathematical Engineering) 是 Stanford 最大的"应用数学+ML"跨系平台, 跨 60+ faculty / 20+ 系。数学背景学生进 AI master 实际路径主走 ICME MS in Computational and Mathematical Engineering（含 ML focus track）+ MS in Stat (DS specialization)。

师资重合详情
MS&E / GSB / CS 共聘教师
全部 joint affiliated
姓名主要方向关系
Susan AtheyEconomics of Technology Professor + Senior Fellow at HAI + SIEPR + Hoover Institution + Founding Director Golub Capital Social Impact Lab + Associate Director HAI; NAS + AAA&S + Econometric Society Fellow; first female John Bates Clark Medal winner; ML × econometrics pioneer (causal forests, generalized random forests)GSB Econ + HAI + SIEPR, NAS, Clark Medal
Mohsen BayatiCarl and Marilynn Thoma Professor of Operations, Information & Technology; applied ML in healthcare + multi-armed banditsGSB OIT, Thoma Chair
Stefan WagerProfessor of OIT (GSB) + Stat courtesy; causal inference + ML; co-creator generalized random forestsGSB OIT + Stat
Lihua LeiAssistant Professor of OIT (GSB) + Stat courtesy; ML + causal inference + multiple testingGSB OIT + Stat
Erik BrynjolfssonJerry Yang and Akiko Yamazaki Professor + Senior Fellow at HAI + Director Stanford Digital Economy Lab + Director SIEPR; NAE Member; "The Second Machine Age" + "Power and Prediction"; AI productivity researchSIEPR Director + HAI + GSB by courtesy, NAE
Amir GoldbergProfessor of Organizational Behavior; ML + cultural sociology + computational social science; teaches AI for org researchGSB OB primary
Yuyan WangAssistant Professor of Marketing; AI + ML for recommender systems + customer behaviorGSB Mktg primary
Kuang XuAssociate Professor of OIT (GSB); ML for healthcare + queueing + statistical learningGSB OIT primary
Hamid NazerzadehAssociate Professor of OIT (GSB); operations research + ML for digital marketplacesGSB OIT primary
Vasilis SyrgkanisAssistant Professor of MS&E + courtesy in CS, EE + ICME affiliated; SAIL + HAI affiliated + Stanford Data Science; ML, causal inference, online learning, RL, mechanism designMS&E + CS + EE + ICME + SAIL
Andrew NgAdjunct Professor of CS (formerly GSB); founder DeepLearning.AI, Coursera, Landing AI; former Baidu Chief ScientistCS Adjunct + AI4ALL/DeepLearning.AI

Stanford 是 8 所中转型 AI 最理想的目的地——不是因为门槛低，而是因为"曲线救国"路径多。如果直接申 CS MS（AI specialization）很难，但 ICME / Statistics MS / BMI / MS&E 都是公认的"AI 友好型"硕士项目，且每一个的 USNews 都在该领域 Top 5。HAI 的存在让跨系合作成为常态。转型最佳路径：理工科 → ICME；统计/数学背景 → Stat MS；生医 → BMI；商科 → MS&E。

来源：cs.stanford.edu · icme.stanford.edu · bmi.stanford.edu · msande.stanford.edu
04

UC Berkeley

加州伯克利 · BAIR Lab（Berkeley AI Research）+ CDSS（Computing, Data Science & Society）学院
USNews CS #1
①

AI program 核心专业课 & Listed Faculty

MEng AI/ML · MS · BAIR

Berkeley 没有独立 "MS in AI"，而是通过 EECS MEng（1 年专业硕士）+ AI/ML 集中方向来培养 AI 工程师。EECS MEng 学生需在 4 门技术课中至少选 3 门来自其 concentration——AI/ML concentration 的核心课如下。

CS 188Introduction to Artificial Intelligence
CS 189Introduction to Machine Learning
CS 280Computer Vision
CS 281A/BStatistical Learning Theory
CS 282ADesigning, Visualizing & Understanding DNN
CS 285Deep Reinforcement Learning
CS 287Advanced Robotics
CS 288NLP
CS 289AIntroduction to Machine Learning (grad)
CS 294-XXSpecial Topics（每学期不同 AI 方向）
EECS 126Probability and Random Processes
EECS 127/227Optimization Models
EECS 183NLP（新课）
EECS 224Foundations of Computer Vision

Listed Faculty（BAIR 核心，节选）：

Pieter Abbeel Jitendra Malik Stuart Russell Michael I. Jordan Alexei Efros Sergey Levine Trevor Darrell Dan Klein Anca Dragan Jacob Steinhardt Moritz Hardt Benjamin Recht Angjoo Kanazawa Sewon Min
②

非 EECS 系硕士生选 AI 课的政策

明文 instructor permission
A · 硬性门槛

核心规则：Berkeley EECS 官网明文规定 "To enroll in a graduate course, contact the professor to receive permission"——所有 EECS 研究生课原则上需要教授书面/口头批准。本科 CS 课更严格："Only CS 168 and CS 188 are considered 'open' to any UC Berkeley undergraduate"（即除了 CS 168 网络和 CS 188 AI 入门外，其余 upper-division CS 课都对非 CS 学生关闭）。

研究生层面：

(1) EECS MEng 学生只能选"approved MEng class list"上的课，非 EECS master 想注册同一门课需 instructor permission。
(2) Statistics MA / Data Science MS 学生想选 CS 189 / 289A / 285 等需 instructor approval；CS 189（Intro ML）热度极高，给非 EECS 学生的位置有限。
(3) Haas MBA / MFE 学生若想选 CS 285 Deep RL 这种课，几乎需要每个具体班次都单独 email 教授。

软化机制：2024 年新成立的 CDSS（Computing, Data Science & Society）学院把 Statistics 和部分 EECS 课程合并管理，CDSS 内部的 master 学生（如 MIDS、Data Science MA）跨系选课摩擦较小。

B · 学位计算

EECS MS 把 elective 限定在 EECS 内部，外系课要 advisor 批准；Statistics MA 的 Data Science Track 已正式认可 CS 189 / 289A / 285 作为 elective；Master of Information & Data Science（MIDS，在线）有自己的 ML / DL / NLP 课，不需要跨系选课。Designated Emphasis (DE) in Computational Data Science & Engineering 是博士生路径，硕士不适用。

来源：eecs.berkeley.edu/academics/courses/approved-cs-graduate-and-special-topics-courses · eecs.berkeley.edu/resources/undergrads/cs/enrollment-policy · eecs.berkeley.edu/resources/grads/meng-2 · cdss.berkeley.edu
③

与 AI 交叉的硕士项目（6 领域）

Berkeley × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MA in Statistics
MS in Statistics – Data Science Concentration
5th-Year MA in Statistics（Berkeley 本科生专属）
Stat MA 是 1 年项目
Stat #2
USNews
 强
CS 189/289 计 Stat 学分≈ 65%
 强
Jordan/Wainwright/Yu 横跨
课程重合详情
EECS AI 课与 Stat MA/MS 的重合
全部 重合 elective 独有
课号课程类型
CS 189 / 289AIntro to Machine Learning重合（Stat MA elective）
STAT 215AStatistical Models重合（与 ML 紧密关联）
STAT 241A / CS 281AStatistical Learning Theory重合（cross-listed）
STAT 241B / CS 281BAdvanced Topics in Learning & Decision Making重合（cross-listed）
STAT 154Modern Statistical Prediction重合
CS 285Deep Reinforcement Learningelective（需 instructor permission）
CS 280Computer Visionelective
STAT 210A/BTheoretical Statistics仅 Stat
STAT 230ALinear Models仅 Stat

Stanford 生物 AI 主体: (1) Department of Biomedical Data Science (DBDS, 2017 立系) 开 MS in Biomedical Data Science (research-track + professional) + PhD; (2) Department of Genetics + Department of Biology; (3) Bioengineering + BMI。James Zou (DBDS, 2025 ISCB Overton Prize)是该系核心 AI PI。Russ Altman 任 BME + Genetics + Medicine + BMI 多系联合, NAS + NAM 双院士, 是 Stanford AI×Bio 的奠基人。Tibshirani + Hastie 跨 Stat ↔ DBDS 双系。

师资重合详情
Stat 系与 EECS AI 共聘师资
全部 joint primary affiliated
姓名主要方向关系
Ryan TibshiraniDepartment Chair (effective July 2025) + Professor of Stat; 2023 COPSS Presidents' Award; lasso pioneer + statistical learning textbook (ISL) co-author; CMU → Berkeley 2022; high-dim statistics, ML, optimization, distribution-free inferenceStat Chair, COPSS Presidents Award
Bin YuDistinguished Professor of Stat + EECS; 2023 COPSS Distinguished Achievement Award (Fisher Lecture); 2014 IMS President; NAS Member; statistical ML, interpretable ML, causal inference, "Veridical Data Sciences towards Trustworthy AI"Stat + EECS, NAS, COPSS DAAL
Michael JordanProfessor of the Graduate School (Stat + EECS); 2020 SIAM John von Neumann Lecture; NAS + NAE + AAA&S; ML pioneer, variational methods, Bayesian nonparametrics, RLStat + EECS, NAS+NAE
Peter BartlettProfessor of the Graduate School (Stat + EECS) + Senior Member at Google DeepMind; statistical learning theory + neural network theory + RL theoryStat + EECS + DeepMind
Peter BickelProfessor of the Graduate School + Professor Emeritus; NAS + AAA&S Member; 1981 COPSS Presidents' Award; high-dim statistics + asymptoticsStat Emeritus, NAS, COPSS 1981
Jennifer ChayesDistinguished Professor + Dean of CDSS (College of Computing, Data Science, and Society); NAS + AAA&S Member; ML theory + algorithmic game theory + applied probability; previously MSR Technical FellowStat + Math + EECS + CDSS Dean, NAS
Mark van der LaanDistinguished Professor of Biostat + Stat; 2005 COPSS Presidents' Award; targeted machine learning (TMLE) creator; super learnerStat + Biostat, COPSS 2005
Yun S. SongProfessor of Stat + EECS; ISCB Fellow Class of 2026; computational biology + ML + applied probability; Chan Zuckerberg BiohubStat + EECS, ISCB Fellow 2026
Jacob SteinhardtAssistant Professor of Stat; AI alignment + ML safety + robust ML; co-founder TransluceStat primary
Jason LeeAssociate Professor of Stat; deep learning theory + foundations of ML + optimizationStat primary
Song MeiAssistant Professor of Stat; deep learning theory + high-dim statistics + ML; NSF CAREERStat primary
Sandrine DudoitProfessor of Biostat + Stat; former Stat Chair (2019-22); computational biology + bioinformatics + biostatistics MLStat + Biostat
Haiyan HuangProfessor of Stat; former Stat Chair (2022-25); statistical genomics + ML for biologyStat former Chair
Peng DingAssociate Professor of Stat; 2023 IMS Emerging Leader Award; causal inference + high-dim statisticsStat, 2023 Emerging Leader
Avi FellerAssociate Professor of Stat + Public Policy (Goldman School); 2023 IMS Emerging Leader Award; causal inference + Bayesian + ML for social scienceStat + Goldman School, 2023 Emerging Leader
Will FithianAssociate Professor of Stat; selective inference + multiple testing + MLStat primary
Fernando PérezAssociate Professor of Stat; creator of IPython / Jupyter Notebook; computational science + ML infrastructureStat primary, Jupyter creator
Alex HuthAssistant Professor of Stat + Neuroscience; computational neuroscience + ML for fMRI + LLM × brain encodingStat + Neuroscience
Michael MahoneyAdjunct Professor of Stat + ICSI + LBNL; large-scale ML + numerical linear algebra + scientific MLStat + ICSI + LBNL
Amanda CostonAssistant Professor of Stat; ML fairness + causal ML + algorithmic decision-makingStat primary
Nikita ZhivotovskiyAssistant Professor of Stat; statistical learning theory + concentration inequalitiesStat primary
Ahmed AlaaAffiliated Assistant Professor (Stat) + Assistant Professor of EECS & UCSF Computational Precision Health; ML for healthcare + generative MLStat + EECS + UCSF
Adam YalaAffiliated Assistant Professor (Stat) + Assistant Professor of EECS & UCSF; ML for medical imaging + breast cancer screening AIStat + EECS + UCSF
Jiantao JiaoAffiliated Assistant Professor (Stat) + Assistant Professor of EECS; information theory + ML + RLStat + EECS
Lexin LiAffiliated Professor (Stat) + Professor of Biostat; ML for neuroimaging + tensor methods + high-dim statisticsStat + Biostat
Liberty HamiltonAssistant Professor of Stat; computational neuroscience + ML for speech and brain encodingStat primary
Rasmus NielsenProfessor of Stat + Integrative Biology; 2026 Guggenheim Fellow; population genomics + statistical genetics + MLStat + IB, 2026 Guggenheim
Ryan GiordanoAssistant Professor of Stat; Bayesian computation + variational inference + MLStat primary
Math
数学
MA in Mathematics（无 AI track，研究型）
无独立的 Applied Math MS
常通过 PhD 中途取得
Math MS 体量小
Math Top 3
USNews
 中
靠 elective≈ 45%
 中
部分纯数研究 ML
课程重合详情
Math MA 课中含 AI 元素或可选的 EECS AI 课
全部 重合 elective 独有
课号课程类型
MATH 270Hot Topics in Mathematics（不定期含 ML 主题）重合（按学期）
MATH 270Topics in Applied Mathematics重合
CS 189Intro to Machine Learningelective（需 advisor 批准）
CS 281A / STAT 241AStatistical Learning Theoryelective
MATH 202A/BTopology仅 Math
MATH 250A/BAlgebra仅 Math

Stanford Chemistry（Mulvane-Ehrsam Wing 内 ~30 教授）AI/ML 转向以 Todd Martínez (NAS, Ehrsam-Franklin Chair) 为旗舰——量子化学 ML 神经网络势能（NN potentials）领域全球开创者之一; Markland 协同维护 SPICE / TorchMD-Net 等 ML 化学开源生态; Grant Rotskoff（2021 加入）做 ML for chemistry + 统计物理 of deep learning。化学背景 master 路径不在 Chem 系内, 而是申 MS in Chemistry（部分 thesis 路径可选 ML PI）, 或转向 ICME / DBDS / CS。

师资重合详情
Math 系做 ML 的教师
全部 joint affiliated
姓名主要方向关系
Lin LinProfessor of Math + Senior Faculty Scientist LBNL; scientific machine learning + quantum chemistry + quantum computation; PECASE awardeeMath + LBNL, PECASE
Jennifer ChayesProfessor of Math + Stat + EECS, Dean of CDSS; NAS Member; ML theory + algorithmic game theoryMath + Stat + EECS + CDSS Dean, NAS
Per-Olof PerssonProfessor of Math; numerical methods + discontinuous Galerkin + ML for PDEsMath primary
Ming GuProfessor of Math; numerical linear algebra + randomized algorithms + MLMath primary
James DemmelProfessor Emeritus of Math + EECS; NAS + NAE; numerical linear algebra + parallel computing for MLMath + EECS Emeritus, NAS+NAE
Lisa GoldbergProfessor of the Practice of Economics in Math; quant finance + ML for risk modeling; Aperio Group co-founderMath primary
Richard KarpProfessor Emeritus of Math + EECS; 1985 Turing Award; NAS + NAE; algorithmic foundations relevant to ML/AIMath + EECS Emeritus, Turing Award
Olga HoltzProfessor of Math; numerical analysis + matrix theory + algorithmsMath primary
Venkatesan GuruswamiProfessor of Math + EECS; theoretical CS + coding theory + algorithm design; Simons InvestigatorMath + EECS
Sunčica ČanićProfessor of Math; applied PDEs + ML for biomedical fluid dynamicsMath primary
Michael J. LindseyAssistant Professor of Math; scientific ML + numerical methods for PDE / quantum systemsMath primary
Bio
生物
MA in Computational Biology
MS in Computational Biology
QB3 California Institute（研究院）
CompBio 项目历史悠久
Bio Top 3
USNews
 强
CompBio 必修含 ML≈ 65%
 强
BAIR + QB3 共享
课程重合详情
CompBio MA/MS 与 EECS AI 的重合
全部 重合 elective 独有
课号课程类型
CMPBIO 201Introduction to Computational Biology重合
CMPBIO 290Special Topics in Comp Bio (含 ML)重合
STAT 246Statistical Methods for Biological Data重合
CS 174Combinatorics & Discrete Probability（用于生物建模）重合
CS 189Intro to Machine Learningelective
CS 285Deep Reinforcement Learning（用于药物发现）elective
MCB 230Cell & Molecular Biology仅 Bio
MCB 232Genetics仅 Bio

Stanford 物理 AI 主体: (1) KIPAC (Kavli Institute for Particle Astrophysics and Cosmology), 由 Risa Wechsler (NAS+AAA&S+APS Fellow+AAAS Fellow) 任 Director; (2) SLAC National Accelerator Laboratory ML Initiative（含 LCLS, ATLAS, DUNE, LSST 等大科学装置 ML 团队, Daniel Ratner 领导）; (3) Center for Decoding the Universe (C4DU, 2024 立项) 由 Stanford Data Science + KIPAC 联合, Wechsler + Susan Clark 共同领导。物理 master 走 MS in Physics (research/thesis track) 在以上集群里选 PI; 或转 ICME MS（Ching-Yao Lai 已是 ICME affiliated, 物理-CS-Stat 桥梁）。

师资重合详情
CompBio + AI 跨界教师
全部 joint primary affiliated
姓名主要方向关系
Jennifer ListgartenProfessor of EECS + Center for Computational Biology + Bioengineering + Steering Committee BAIR; ISCB Fellow Class of 2026; AI for protein design + generative models for biologyEECS + CCB + BAIR, ISCB Fellow 2026
Yun S. SongProfessor of Stat + EECS; ISCB Fellow Class of 2026; AI/ML for population genomics, computational biology, Chan Zuckerberg BiohubStat + EECS, ISCB Fellow 2026
Lior PachterVisiting Professor (formerly tenured at Berkeley) + Caltech Bren Professor; computational genomics + scRNA-seq ML methods (kallisto, bustools)Caltech (now)
Nir YosefAssociate Professor of EECS + CCB (now Weizmann); scVI / Cassiopeia / single-cell MLEECS + CCB
Rasmus NielsenProfessor of Integrative Biology + Stat; 2026 Guggenheim Fellow; population genetics + ML for evolutionary inferenceIB + Stat, 2026 Guggenheim
Mark van der LaanDistinguished Professor of Biostat + Stat; targeted machine learning (TMLE) for biomedical inferenceBiostat + Stat, COPSS 2005
Sandrine DudoitProfessor of Biostat + Stat; computational biology ML + bioinformaticsBiostat + Stat
Haiyan HuangProfessor of Stat; statistical genomics + ML for biologyStat primary
Anne BrunetAdjunct Professor of MCB; aging biology + computational genomics MLMCB primary
Jasmin FisherAdjunct Professor + Microsoft Research; computational biology + ML for cellular systemsMCB + MSR
Ahmed AlaaAssistant Professor of EECS + Computational Precision Health (UCSF/Berkeley); ML for healthcare + generative ML for medical dataEECS + UCSF
Adam YalaAssistant Professor of EECS + Computational Precision Health; ML for medical imaging + cancer screening AI (Mirai, Sybil)EECS + UCSF
Aaron StreetsAssociate Professor of Bioengineering + CCB; single-cell genomics + MLBioE + CCB
Chem
化学
无 Chem MS 走 AI 路径
MS in Materials Science & Engineering（含 ML for Materials）
MEng in NSE（核工程，部分含 ML）
Chem MS 极少
Chem Top 5
USNews
 弱
仅个别课< 25%
 弱
无系统交叉
课程重合详情
化学/材料 master 中触及 AI 的课
全部 重合 elective 独有
课号课程类型
MAT SCI 215Computational Materials重合（含 ML 模块）
CHEM 273Chemoinformatics重合
CS 189Intro MLelective（需 advisor 批准）
CHEM 220AQuantum Mechanics仅 Chem

Stanford 商科 AI 主入口是 GSB MSx + MBA + PhD 而非独立 MS in Business Analytics（Stanford 不开 MSBA）。商科背景 master 路径主要走 MS&E (Management Science & Engineering)（工程院, 含数据科学 + 金融工程方向, 与 ICME / SAIL 关系密切）+ MS in Statistics: Data Science specialization。Susan Athey (Clark Medal + NAS) 是 GSB ML×Econ 旗舰。Erik Brynjolfsson 2020 自 MIT Sloan 转 Stanford 任 SIEPR Director + Digital Economy Lab Director, NAE 院士。MS&E 的 Vasilis Syrgkanis 是 SAIL + ICME + CS + EE + HAI 多系桥梁。

师资重合详情
化学/材料 × ML 教师
全部 joint affiliated
姓名主要方向关系
Teresa Head-GordonChancellor's Professor of Chemistry + Bioengineering + ChemE; theoretical chemistry + ML + chemical physics + biomolecules + materials; ACS Fellow + APS Fellow + AAAS FellowChem + BioE + ChemE
Martin Head-GordonKenneth S. Pitzer Distinguished Professor of Chemistry; NAS Member; theoretical chemistry + electronic structure + Q-Chem (commercial QC software)Chem, NAS
K. Birgitta WhaleyProfessor of Chemistry + Director Berkeley Quantum Information & Computation Center; quantum information + quantum computation + ML for quantum systemsChem + BQIC Director
Eran RabaniProfessor of Chemistry; theoretical chemistry + computational chemistry + ML for nanomaterialsChem primary
Gerbrand CederSamsung Distinguished Chair + Professor of Materials Science & Engineering + Senior Faculty Scientist LBNL; ML for materials discovery (Materials Project co-founder)MSE + LBNL, Samsung Chair
David LimmerAssociate Professor of Chemistry; statistical mechanics + ML for nonequilibrium chemistryChem primary
William A. Lester Jr.Professor Emeritus of Chemistry; quantum Monte Carlo + computational methodsChem Emeritus
Markita LandryAssociate Professor of Chemical & Biomolecular Engineering; ML × chemical biology + molecular sensors + nanotechnology; NIH PioneerChemE + CCB
Phys
物理
无 Physics MS（PhD 直接路径）
BAIR Physics × ML 研究型合作
NSF Center for Theoretical AI（部分）
物理 master 不存在
Phys Top 3
USNews
 弱
仅 PhD 路径< 30%
 中
BAIR 部分交叉
课程重合详情
物理涉及 AI 的课
全部 重合 elective 独有
课号课程类型
PHYSICS 290Special Topics（按学期）重合（按学期）
CS 189Intro MLelective
CS 285Deep RLelective
PHYSICS 221A/BQuantum Mechanics仅 Phys

Berkeley Statistics Department（Evans Hall, 2026 迁入 The Gateway 新楼）是全美最早建立的统计系之一（Neyman 1955 立系）, 已获 2 项 National Medal of Science + 12 名 NAS 院士。系属 CDSS（College of Computing, Data Science, and Society, 2023 立院, Jennifer Chayes 任院长）。开 MA in Statistics & Data Science (MA SDS)。Tibshirani 2025-7 月起任 Stat Chair（接替 Haiyan Huang）, lasso 创始人 + ISL 教科书作者。系研究关键词页公开列出 ML, causal inference, high-dim, statistical learning 等 AI 关键词。Bin Yu (NAS, 2023 COPSS DAAL) + Mike Jordan (NAS+NAE, ML 教父) + van der Laan (TMLE 创始人) 共同构成全美最强 Stat-ML 集群。

师资重合详情
Physics × ML 教师
全部 joint affiliated
姓名主要方向关系
Uros SeljakProfessor of Physics + Astronomy + LBNL; NAS Member; cosmology + CMB + ML for cosmological parameter inference; co-author of Particle Data Group ML chapter (2025)Physics + Astronomy + LBNL, NAS
Saul PerlmutterFranklin W. and Karen Weber Dabby Professor of Physics + LBNL; 2011 Nobel Prize in Physics; NAS + AAA&S + APS Fellow; data-intensive astronomy + ML for supernovae cosmologyPhysics + LBNL, Nobel 2011
Martin WhiteProfessor of Physics + Astronomy; cosmology + large-scale structure + ML emulators for cosmological surveys (DESI, Rubin)Physics + Astronomy
K. Birgitta WhaleyProfessor of Chemistry; quantum computing + ML for quantum systemsChem + BQIC
Norman YaoAssociate Professor of Physics; quantum many-body physics + ML for quantum simulation; (now Harvard, courtesy Berkeley)Physics primary
Joshua BloomProfessor of Astronomy + LBNL; time-domain astrophysics + ML for transient classification (RAPID, autoencoders); co-founder Wise.ioAstronomy + LBNL
Kasper van WijkAstronomy adjunct; ML applications in seismology/astronomyAstronomy
Maurice Garcia-SciveresSenior Scientist LBNL Physics Division; ATLAS detector + ML for HEP triggersLBNL primary
Ehud AltmanProfessor of Physics; condensed matter + ML for strongly correlated systemsPhysics primary
Biz
商科
Master of Financial Engineering (MFE, Haas)
Master of Engineering with EECS Mfg Engineering（含 AI track）
无传统 MSBA
MFE 是顶级量化金融硕士
Haas MBA #7 / MFE Top 3
USNews / Risk.net
 中
MFE 含 ML 必修≈ 50%
 中
MFE 教师跨 EECS
课程重合详情
MFE / MEng 中的 ML 课
全部 重合 elective 独有
课号课程类型
MFE 230IFinancial Risk Measurement & Mgmt（含 ML）重合
MFE 230NMachine Learning & Data Analysis in Finance重合（MFE 必修）
MFE 230TAdvanced Topics in Quant Finance（部分 ML）重合
CS 189Intro MLelective
IEOR 165Engineering Statistics & Decision Makingelective
UGBA 232Marketing Analytics（含 ML）仅 Haas

Berkeley Math（970 Evans, ~80 教授）以纯数学传统强势, AI/ML 桥梁主要通过 (1) Applied Math 方向（Lin Lin, Persson, Gu, Demmel, Holtz, Lindsey）做 scientific ML + 数值方法 + ML 理论; (2) Lisa Goldberg（Practice of Economics）+ Aperio 联合 quant ML; (3) Jennifer Chayes 跨 Math/Stat/EECS 三系任 CDSS Dean。数学背景 master 路径主要走 MA in Mathematics（applied track）+ Stat MA SDS + CDSS MIDS（在线数据科学硕士）。

师资重合详情
Haas / EECS / Stat 共聘
全部 joint affiliated
姓名主要方向关系
Lisa GoldbergProfessor of the Practice of Economics + Co-Director Consortium for Data Analytics in Risk (CDAR); quantitative finance + ML for risk + factor investing; Aperio Group co-founderMath + Haas CDAR Co-Director
Ananth MadhavanExecutive Director of Berkeley MFE Program; quantitative finance + ML for trading + market microstructure; ex-BlackRock Global Head of ResearchHaas MFE Director
Adair MorseSoloman P. Lee Chair in Business Ethics + Associate Professor of Finance; fair AI in credit markets + fintech MLHaas Finance, Lee Chair
Anastassia FedykAssistant Professor of Finance; ML for firm-level innovation + AI hiring + systemic riskHaas Finance
Zsolt KatonaProfessor of Marketing + Cheryl & Christian Valentine Faculty Fellow + Chair Haas AI Task Force; AI in market research + computational advertising (PhD in CS + PhD in Marketing)Haas Mktg
Sameer SrivastavaEwald T. Grether Professor of Business Administration and Public Policy + Co-Director Computational Culture Lab; ML × cultural analytics + organizational behaviorHaas Mgmt of Org, Grether Chair
Jonathan KolstadEgon and Joan von Kaschnitz Distinguished Professor + Director Berkeley Center for Healthcare Marketplace Innovation (joint with CDSS); AI in healthcareHaas Econ, von Kaschnitz Chair
Toby StuartHelzel Chair Professor + Director Berkeley Haas Entrepreneurship Program; AI startups + technology entrepreneurship; head of Mgmt of OrgHaas Mgmt of Org, Helzel Chair
Steven TadelisJames J. and Marianne B. Lowrey Chair in Business + Professor of Economics; digital marketplaces + ML for platform economics; ex-eBay Senior DirectorHaas Econ, Lowrey Chair
Hal VarianProfessor Emeritus + Chief Economist at Google; "Machine Learning & Economics"; AAA&S; pioneer of digital economicsHaas Emeritus + Google
Thomas LeeAssociate Adjunct Professor + Research Scientist (Haas OIT Group); data science + AI applications in businessHaas OIT
Pieter AbbeelProfessor of EECS + Director Berkeley Robot Learning Lab + Co-Director BAIR; (cross-listed in Haas AI program); robotics + RL + LLM agents; co-founder Covariant + Embodied AIEECS + BAIR + Haas AI Cert

Berkeley 是 Top 8 中最适合"理工科+统计学"背景的转型目的地——Statistics MA、ICBS、CompBio MA 都直接和 BAIR 共享师资。但作为公立校，跨系选 AI 课的"实际可达性"低于 Stanford，因为 EECS 课程容量有限、优先内部学生。转型最佳路径：Stat 背景 → Stat MA（DS Track）；Math/Phys/Eng → MEng EECS（AI/ML concentration）；Biz → 联合学位 MBA + EECS / MEng with Fung。

来源：bair.berkeley.edu · statistics.berkeley.edu/programs/grad/masters · ccb.berkeley.edu
05

Georgia Institute of Technology

佐治亚理工 · OMSCS（在线 MSCS）规模全美最大；CSE 学院专为跨学科 AI 设计
USNews CS #5
①

AI program 核心专业课 & Listed Faculty

MSCS · OMSCS · MS Analytics

GTech 提供两个 AI specialization：MSCS 内的 "Specialization in Machine Learning" 和 "Specialization in Artificial Intelligence"。两者共享 ML 核心，但 AI specialization 偏 cognitive / interactive intelligence。OMSCS 是同一课程的在线版（学位完全相同）。

CS 7641Machine Learning（必修）
CSE 6740Computational Data Analysis
CS 7642Reinforcement Learning & Decision Making
CS 7643Deep Learning
CS 7644Machine Learning for Robotics
CS 7646ML for Trading
CS 7647ML with Limited Supervision
CS 7650Natural Language Processing
CS 6476Computer Vision
CS 6603AI, Ethics, and Society
CS 7616Pattern Recognition
CS 7626Behavioral Imaging
CS 7631/2/3Multi-Robot / Game AI / HRI
CS 7651Human and Machine Learning
CS 7545Machine Learning Theory
CSE 6240Web Search and Text Mining
CSE 6242Data and Visual Analytics
CSE 6250Big Data for Health
ISYE 6740Computational Data Analysis（ISYE 版）

Listed Faculty（CoC ML/AI 核心，节选）：

Charles Isbell Mark Riedl Sonia Chernova Devi Parikh Dhruv Batra Le Song Polo Chau Bistra Dilkina Jacob Eisenstein James Rehg
②

非 CS 系硕士生选 AI 课的政策

Top 8 中最开放
A · 硬性门槛

核心规则：GTech 的 AI 研究生课（CS 7641 ML、CS 7643 DL、CS 7642 RL、CSE 6740 Computational Data Analysis）没有"必须是 CS 学生"的硬性 enrollment 限制——只检查 prerequisites（线代 + 概率统计 + Python），且可由 advisor 或 graduate coordinator 认证已修等价课。

GTech 的制度设计本身就为跨学科：MSCSE 在 11 个 home unit（Aerospace / Biology / Chemistry / Civil Eng / CSE / ECE / ISYE / Materials / Math / ME / Biomedical Eng）下提供同一个 MS 学位——意味着 Math 系学生申请并被录入 MSCSE 后，可以直接选 CS 7641、CSE 6740 这种 AI 课作为 specialization，零摩擦。

对于已经在非 MSCSE master（如 MS Math、MS Physics、Scheller MBA）的学生，跨系选 AI 课需要：(1) advisor 批准；(2) 课程 instructor 在 capacity 允许时同意。CS 7643 的官网原话："Talk to the advisor or graduate coordinator for your academic program. They are keeping track of your degree requirements."——意味着程序简单且文档化。

OMSCS（在线 MSCS）是终极转型路径：完全不限本科背景，"undergraduate degree need not be in CS"，每学期开放招生，整个项目就是为非 CS 背景设计。

B · 学位计算

GTech 政策："up to six credit hours (two courses) to be counted toward two different MS degrees"——这是 Institute-level 政策。MSQCF（Quantitative & Computational Finance）和 MSCSE 之间还有特殊的 "Shared Credit Agreement"，允许双计 12 学分。这种跨硕士学位的双计在 8 校中非常罕见。

MS Analytics（MSA）的 elective 列表里 CS 7641、CS 7643、CSE 6242 都明确认可。MS Bioinformatics 课表也列入 CS 7641 / CSE 6250 Big Data for Health。

来源：catalog.gatech.edu/academics/graduate/masters-degree-info · catalog.gatech.edu/programs/computational-science-engineering-ms · faculty.cc.gatech.edu/~zk15/teaching/AY2025_cs7643_spring · omscs.gatech.edu
③

与 AI 交叉的硕士项目（6 领域）

GTech × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MS in Statistics（ISYE 系）
MS in Analytics（MSA, 跨 CoC + Scheller + ISYE 三方）
注意：GTech 不是 USNews 排名 Top Stat 项目，但与 ISYE 强势关联
Statistics 与 ISYE 紧密
Industrial Eng/OR #1
USNews
 强
MSA 即 ML 主导≈ 65%
 强
ISYE 与 CoC 联合
课程重合详情
CS AI 课与 ISYE/MSA 的重合
全部 重合 elective 独有
课号课程类型
CS 7641Machine Learning重合（MSA 必修可选）
ISYE 6740 / CSE 6740Computational Data Analytics重合（cross-listed）
ISYE 6420Bayesian Statistics重合
ISYE 7406Data Mining & Statistical Learning重合
ISYE 6739Statistical Methods重合
CS 7643Deep Learningelective
CS 7642Reinforcement Learningelective
ISYE 6644Simulation仅 ISYE

Berkeley 生物 AI 主体 = Center for Computational Biology (CCB)（2003 立, 跨 14 系 6 院 40+ 教授）+ Department of Molecular and Cell Biology + Department of Bioengineering + School of Public Health (Biostat)。CCB 开 PhD in Computational Biology（master 通常通过 Bioengineering MS 或 Stat MA + CCB designated emphasis）。Jennifer Listgarten + Yun S. Song 同时入选 ISCB 2026 Fellow Class。Listgarten 是 BAIR 指导委员, AI 蛋白质设计领军者。

师资重合详情
ISYE + CoC ML 共聘教师
全部 joint affiliated
姓名主要方向关系
Yao XieA. Russell Chandler III Early Career Professor + Associate Professor ISyE + Associate Director ML & Data Science at ML@GT; change-point detection + ML for time series + signal processingISyE + ML@GT Assoc Director
Guanghui (George) LanA. Russell Chandler III Chair + Professor ISyE + Associate Director ML & Statistics at ML@GT; stochastic optimization + ML algorithms + RL theory; INFORMS Optimization Society AwardISyE + ML@GT Assoc Director, Chandler Chair
Tuo ZhaoAssociate Professor ISyE; deep learning theory + non-convex optimization + NLP/LLM + scientific computing; ML@GT coreISyE primary
Arkadi NemirovskiHightower Chair Professor ISyE + John von Neumann Theory Prize 2003 + Dantzig Prize 2000; NAS Member + NAE; convex optimization + interior-point methods (foundations of ML)ISyE, Hightower Chair, NAS+NAE
Alexander ShapiroProfessor ISyE; NAS Member; stochastic programming + statistics + risk-averse optimization for MLISyE, NAS
Xiaoming HuoProfessor ISyE + NSF Program Director (rotator); statistical learning + high-dim data + computational statsISyE primary
Jianjun (Jan) ShiCarolyn J. Stewart Chair + Professor ISyE; NAE Member; data fusion + manufacturing AI/ML + system informaticsISyE, NAE, Stewart Chair
Roshan JosephA. Russell Chandler III Chair Professor ISyE; experimental design + ML emulation for engineering systemsISyE, Chandler Chair
Yu DingAnderson-Interface Chair + Professor ISyE; NAE Member; data analytics + ML for renewable energy systemsISyE, Anderson-Interface Chair, NAE
Vidya MuthukumarAssistant Professor ISyE + ECE; statistical learning theory + game theory + ML safety; NSF CAREERISyE + ECE
Ashwin PananjadyAssistant Professor ISyE + ECE; statistical learning theory + RL theory + high-dim statisticsISyE + ECE
Sen NaAssistant Professor ISyE; stochastic optimization for ML + scientific MLISyE primary
Dmitrii OstrovskiiAssistant Professor ISyE; statistical learning theory + non-parametric estimationISyE primary
Katya ScheinbergCoca-Cola Foundation Chair + Professor ISyE; nonlinear optimization + ML algorithms + ICML/NeurIPS area chair; INFORMS Computing Society PrizeISyE, Coca-Cola Chair
Shihao YangHarold R. and Mary Anne Nash Junior Faculty Endowed Professor + Assistant Professor ISyE; statistical inference + ML for epidemiology + dynamical systemsISyE, Nash Faculty
Eunhye SongCoca-Cola Foundation Early Career Faculty + Assistant Professor ISyE; simulation optimization + ML for stochastic systemsISyE, Coca-Cola Faculty
Enlu ZhouHarold R. and Mary Anne Nash Professor + Professor ISyE; simulation + Bayesian + ML for stochastic optimizationISyE, Nash Chair
Kamran PaynabarGeorgia Power Professor + Professor ISyE; statistical ML for industrial systems + manufacturing AI + functional data analysisISyE, Georgia Power Chair
Nicoleta SerbanProfessor ISyE; functional data + high-dim statistics + ML for healthcareISyE primary
Juba ZianiAssistant Professor ISyE; algorithmic game theory + ML fairness + privacy + mechanism designISyE primary
Siva Theja MaguluriFouts Family Early Career Professor + Associate Professor ISyE; stochastic networks + RL theory + queueingISyE, Fouts Faculty
Xiao LiuAssociate Professor ISyE; data analytics + ML for engineering systemsISyE primary
Xiaochen XianAssistant Professor ISyE; statistical ML for monitoring + change-point detectionISyE primary
Math
数学
MS in Mathematics（含 Quantitative & Computational Finance 方向）
MSCSE with Math home unit（最直接的 AI 路径）
MS in Computational Science & Engineering（CSE 系，跨学科）
MSCSE 11 个 home unit 中 Math 是其一
Math #25
USNews
 强
MSCSE 直接走 AI≈ 70%
 中
Math + CSE 部分共聘
课程重合详情
Math MS / MSCSE-Math 中的 AI 课
全部 重合 elective 独有
课号课程类型
CSE 6740 / ISYE 6740Computational Data Analytics重合（MSCSE 必修选项）
CS 7641Machine Learning重合（MSCSE 选）
CSE 6643Numerical Linear Algebra重合
MATH 6263Testing Statistical Hypotheses重合
CS 7643Deep Learningelective
MATH 6121Algebra I仅 Math
MATH 6321Complex Analysis仅 Math

Berkeley College of Chemistry（独立 College, 含 Chemistry + ChemE 两系 + Pitzer Center for Theoretical Chemistry）AI 桥梁: (1) Teresa Head-Gordon 是化学 ML + 蛋白质模拟 + 力场学习的国际领导者; (2) Martin Head-Gordon (NAS) 通过 Q-Chem 商业 QC 软件支撑全球计算化学界; (3) K. Birgitta Whaley 主导 BQIC 量子计算中心; (4) MSE 系 Gerbrand Ceder（Samsung Chair）联合 LBNL 维护 Materials Project（ML 材料发现全球最大数据库）。化学背景 master 走 MS in Chemistry / ChemE。

师资重合详情
数学 × ML 教师
全部 joint affiliated
姓名主要方向关系
Molei TaoAssociate Professor School of Math + ML@GT + ARC; deep learning, optimization, sampling, measure transport, generative modeling, AI4Science; AI4Science集群核心Math + ML@GT + ARC
Wenjing LiaoAssociate Professor School of Math + ML@GT; applied & computational math, data sciences, machine learning; ICML, NeurIPS publications on deep learning theoryMath + ML@GT
Wei ZhuAssociate Professor School of Math; scientific machine learning, statistical learning theory, generative models, optimizationMath primary
Hannah ChoiAssistant Professor School of Math; mathematical and computational neuroscience, Neuro AI, Neuromorphic computingMath primary
Sung Ha KangProfessor School of Math; applied & computational math, imaging sciences, data sciencesMath primary
Yingjie LiuProfessor School of Math; numerical methods and machine learning for solving PDEsMath primary
Haomin ZhouProfessor School of Math; applied & computational math, optimal transport, AI for PDEsMath primary
Rachel KuskeProfessor + School Chair School of Math; applied dynamical systems + data sciencesMath School Chair
Martin ShortProfessor School of Math; applied & computational math, data sciences, social sciences MLMath primary
Luca DieciProfessor School of Math; applied & computational math, optimal transportMath primary
Galyna LivshytsAssociate Professor School of Math; high-dim probability + geometry (foundations of ML)Math primary
Santosh VempalaFrederick Storey Chair Professor of CS + courtesy in Math; AMS Fellow + ACM Fellow + 2024 Mathematical Optimization Society Fulkerson Prize; 2024 Simons Investigator; ML theory + algorithms + optimization (founder ARC)CS + Math, Storey Chair, AMS+ACM Fellow
Bio
生物
MS in Bioinformatics（CoC + Bio 联合）
MS in Quantitative & Computational Bio
MSCSE with Biological Sciences home unit
Bioinformatics 顶级强项
Bio Eng/Bioinformatics #5
USNews
 强
必修含 ML≈ 75%
 强
CoC+Bio 联合教席多
课程重合详情
Bioinformatics MS 与 CoC AI 课重合
全部 重合 elective 独有
课号课程类型
CSE 6250Big Data for Health Informatics重合（必修）
CSE 6242Data & Visual Analytics（必修）重合
CS 7641Machine Learning重合（电子选修）
BIOL 7210Computational Genomics重合
CS 7643Deep Learningelective
CSE 6740Computational Data Analyticselective
BIOL 6500Biostatistics仅 Bio
BIOS 7050Cellular Biology仅 Bio

Berkeley 物理 AI 主体 = Physics Department（45 senate faculty, 9 Nobel 得主历史）+ Astronomy Department + Lawrence Berkeley National Laboratory (LBNL)（特别是 Physics Division + Computational Research Division）。Saul Perlmutter (2011 Nobel) + Uros Seljak (NAS) 共同领导 cosmology ML 集群; Seljak 还是 2025 Particle Data Group "Machine Learning" chapter 共同作者。物理 master 走 MS / PhD in Physics + LBNL 联合 PI; 跨向 AI 也可申 EECS（BAIR 物理学背景 PhD 较多）。

师资重合详情
CoC × Bio 跨界教师
全部 joint primary affiliated
姓名主要方向关系
Mark BorodovskyRegents' Professor of BME + Biological Sciences + CSE; founder of GeneMark gene-prediction software (used worldwide); ML algorithms for biological sequence analysisBME + BIO + CSE Regents
Jeffrey SkolnickRegents' Professor + Mary and Maisie Gibson Chair + GRA Eminent Scholar in Computational Systems Biology + Director Center for the Study of Systems Biology; computational structural biology + ML for protein structureBIO Regents, Gibson Chair, GRA Eminent
Joshua WeitzTom and Marie Patton Chair in Biological Sciences + Professor + Co-Director Quantitative Biosciences PhD + Blaise Pascal International Chair (École Normale Supérieure); theoretical & computational biology + ML for ecology & viral dynamicsBIO, Patton Chair, Blaise Pascal Chair
Greg GibsonTom and Marie Patton Chair (former) + Professor BIO; statistical genomics + MLBIO primary
Peng QiuAssociate Professor BME + ML@GT; computational biology + machine learning + AI for biology and medicineBME + ML@GT
Cassie MitchellAssociate Professor BME; cardiac arrhythmia modeling + scientific ML + computational physiology + HPCBME primary
Pinar KeskinocakWilliam W. George Chair + Regents' Professor ISyE + Co-Founder Center for Health and Humanitarian Systems; NAE Member; ML + OR for health systems and infectious disease modelingISyE Regents, George Chair, NAE
Eva DyerAssociate Professor BME + Coulter Department + ML@GT; ML for neuroscience + representation learning + brain dataBME + ML@GT
Constantine DovrolisProfessor of CS + BIO; graph ML for biology + network systemsCS + BIO
Hang LuCecile L. and David I.J. Wang Professor + School Chair School of Chemical & Biomolecular Engineering; ML for high-throughput biology + microfluidics + neural circuit imagingChBE School Chair, Wang Chair
Hyunjik YangAssistant Professor BME; computational biology + ML for genomicsBME primary
Chem
化学
MS in Chemistry & Biochemistry（罕见，主要 PhD）
MSCSE with Chemistry & Biochem home unit（明文路径！）
MS in Materials Science & Engineering（含 ML for Materials）
MSCSE 提供化学 home unit
Chem Top 15
USNews
 中
仅 MSCSE 路径≈ 50%
 中
Materials 系交叉强
课程重合详情
Chem MSCSE / MSE 中的 AI 课
全部 重合 elective 独有
课号课程类型
CSE 6730Modeling & Simulation重合（化学+ML 应用）
CSE 6643Numerical Linear Algebra重合
CSE 6740Computational Data Analytics重合
CS 7641Machine Learningelective
CHEM 6781Statistical Mechanics仅 Chem
MSE 6411Computational Materials Science仅 MSE

Berkeley Haas School of Business（145+ 全职教授）AI 集群: (1) Master of Financial Engineering (MFE) ——课程嵌入 ML/AI/区块链/DeFi, QuantNet 全美第一并列; (2) 2025 秋启动 AI for Business graduate certificate（30 门选修课, FT/EW/Exec MBA 通用, Erika Walker 副院长牵头, Zsolt Katona 联合教 Business for AI 核心课）; (3) Marketing + OBM + Finance + Econ 多 group 都有 AI 教授, BAIR 的 Pieter Abbeel + Sameer Srivastava 共同教 AI 课。商科背景 master 走 MFE（quant ML）或 MBA + AI for Business 证书。

师资重合详情
Chem × ML 教师
全部 joint
姓名主要方向关系
David SherrillRegents' Professor School of Chemistry & Biochemistry; quantum chemistry + theoretical chemistry + machine learning + Psi4 lead developerChem Regents
Jesse McDanielAssociate Professor School of Chemistry; molecular dynamics + Monte Carlo + quantum chemistry + machine-learning + electrochemistryChem primary
Joshua KretchmerAssistant Professor School of Chemistry; quantum dynamics + non-adiabatic dynamics + quantum embedding; ML methods in theoretical chemistryChem primary
James GumbartProfessor School of Physics + Chemistry; molecular dynamics + protein dynamics + bacteria-specific systems; ML-augmented MDPhysics + Chem
Jean-Luc BrédasRegents' Professor School of Chemistry & Biochemistry; NAE Member + AAAS Fellow + APS Fellow; theoretical chemistry of organic semiconductors + ML for materialsChem Regents, NAE
Hang LuCecile L. & David I.J. Wang Professor + ChBE School Chair; ML for chemical and biomolecular engineering, high-throughput screeningChBE School Chair, Wang Chair
Andreas BommariusProfessor ChBE; biocatalysis + protein engineering + ML for enzyme designChBE primary
Yongjun YangAssistant Professor ChBE; ML × catalysis + materials informaticsChBE primary
Phys
物理
MS in Physics（罕见）
MSCSE with Physics home unit（明文路径！）
MS in Computational Science & Engineering
MSCSE 提供 Physics home unit
Phys Top 30
USNews
 中
仅 MSCSE 路径≈ 50%
 中
量化金融师资交叉
课程重合详情
Phys 涉及的 AI 课
全部 重合 elective 独有
课号课程类型
CSE 6740Computational Data Analytics重合（MSCSE-Phys 必修选项）
CSE 6730Modeling & Simulation重合
CS 7641Machine Learningelective
PHYS 6124Mathematical Methods仅 Phys
PHYS 6125Methods of Theoretical Physics仅 Phys

Georgia Tech 不设独立 Statistics Department。统计 + 数据科学 master 主体走 H. Milton Stewart School of Industrial and Systems Engineering (ISyE)——U.S. News 美国 IE/OR 系排名第一已逾 30 年; ISyE 开 MS in Statistics（与 College of Sciences 联合）+ MS in OR + 跨院 MS in Analytics + MS in CSE。ML@GT (Machine Learning Center) 由 Yao Xie + George Lan 共任 Associate Directors (ML & Data Science / ML & Statistics)。Nemirovski (NAS+NAE, von Neumann Theory Prize) + Shapiro (NAS) + Yu Ding (NAE) + Jan Shi (NAE) 共四位 NAS/NAE 院士驻 ISyE。

师资重合详情
Physics × ML 教师
全部 affiliated
姓名主要方向关系
Daniel GoldmanDunn Family Professor + Professor School of Physics + Co-Director CRAB (Center for Robotics & Bio-systems); NAS Member + APS Fellow; physics-informed ML for legged robotics + active matterPhysics, NAS, Dunn Chair
Flavio FentonProfessor School of Physics; cardiac dynamics + nonlinear physics + ML for cardiac arrhythmias; APS FellowPhysics primary
Predrag CvitanovićGlen P. Robinson Chair Professor School of Physics; nonlinear dynamics + chaos theory + ML for dynamical systemsPhysics, Robinson Chair
Roman GrigorievProfessor School of Physics; nonlinear dynamics + ML for fluid systemsPhysics primary
Glenn SmithProfessor School of Physics; computational biology + machine learning for genomicsPhysics primary
Pablo LagunaAdjunct + School Chair Emeritus School of Physics; computational astrophysics + numerical relativity + ML for gravitational waves; APS FellowPhysics School Chair Emeritus
John WiseProfessor School of Physics; computational cosmology + ML emulators for galaxy formationPhysics primary
Tamas SarlosAssociate Professor School of Physics; ML for high-energy physics + ATLASPhysics primary
Biz
商科
MS in Analytics (MSA, 跨 CoC + Scheller + ISYE)
MS in Quantitative & Computational Finance (QCF)
Scheller MBA + AI specialization
MSA 是顶级 AI 商科项目
Business Analytics Top 5
USNews
 强
MSA 即 ML+OR+商业≈ 70%
 强
三方教师联合
课程重合详情
MSA / QCF 中的 AI 课
全部 重合 elective 独有
课号课程类型
CS 7641Machine Learning（MSA 选修核心）重合
CSE 6242Data & Visual Analytics（MSA 必修）重合
ISYE 6740 / CSE 6740Computational Data Analytics重合
CS 7643Deep Learningelective
MGT 6203Data Analytics in Business（MSA Track 必修）重合（Scheller 商业 ML）
MGT 6748Applied Analytics Practicumelective
ISYE 6644Simulation仅 ISYE

Georgia Tech School of Math 设独立 AI4Science initiative（专为应用数学 ML 集群命名的研究方向）, 集群成员 Tao + Liao + Wei Zhu + Choi + Kang + Yingjie Liu + Zhou + Short 都在 ML@GT 列表内。Math 系开 MS Math, 数学背景 master 也常通过 跨院 MS in Computational Science and Engineering (CSE) 进入 AI/ML（CSE 是独立 School 在 College of Computing 内）+ MS in Quantitative and Computational Finance (QCF, top 10)。Vempala (CS + courtesy Math) 是 ARC Algorithms-Combinatorics-Optimization 中心创始主任。

师资重合详情
CoC + Scheller + ISYE 跨界
全部 joint primary
姓名主要方向关系
Adithya PattabhiramaiahSharon A. and David B. Pearce Associate Professor of Marketing + Inaugural Faculty Director, Center for AI in Business (formerly Business Analytics Center); media monetization + digital/social media marketing + AI-enabled decision-making; teaches "AI and ML Applications in Marketing"Scheller Mktg, Pearce Chair, AI Center Director
Sudheer ChavaAlton M. Costley Chair + Professor of Finance + Founding Director Center for Finance and Technology + Director MS QCF + Adjunct in CSE/CoC; fintech + credit risk + ML for finance; Federal Reserve Bank Atlanta committeeScheller Finance + CSE Adjunct, Costley Chair, MS QCF Director
Eric OverbyCatherine and Edwin Wahlen Family Chair + Professor of IT Management + Senior Associate Dean for Faculty & Research; digital business strategy + ML × digital marketsScheller IT, Wahlen Chair
Deven DesaiSue and John Staton Professor of Law and Ethics + Associate Director ML@GT (Legal, Policy, Ethics, and ML); AI ethics + IP × ML; ex-Google Academic Research CounselScheller Law/Ethics + ML@GT, Staton Chair
Saby MitraSteven A. Denning Chair Professor of IT + previously Sr Assoc Dean; ML × digital platforms + healthcare informaticsScheller IT, Denning Chair
Steven SalbuCecil B. Day Chair + Professor of Strategic Management; AI ethics + business lawScheller Strategy, Day Chair
Han ZhangSteven A. Denning Professor of IT; e-commerce + ML for online platforms + recommender systemsScheller IT, Denning Prof
Sridhar NarayananProfessor of Marketing; quantitative marketing + ML for advertising and pricingScheller Mktg
Andre CalmonAssociate Professor of Operations Management; data-driven operations + ML for supply chainScheller OM
David JoynerExecutive Director of Online Education and OMSCS at College of Computing + Adjunct Faculty Scheller; AI-driven online education + intelligent tutoring; teaches AI for BusinessCoC + Scheller
Nicholas HundAssistant Professor of Operations Management; ML for healthcare operationsScheller OM

GTech 是 8 所中"转型友好度"最高的——原因有三：(1) MSCSE 直接为跨学科设计，11 个 home unit 几乎覆盖 STEM 所有领域；(2) OMSCS 在线无背景限制；(3) MS Analytics（顶级）+ MS Bioinformatics（顶级）作为应用导向入口。转型最佳路径：理工科任意背景 → MSCSE（home unit 选你的本系）；商科 → MS Analytics；预算/灵活性优先 → OMSCS。

来源：cse.gatech.edu · analytics.gatech.edu · bioinformatics.gatech.edu
06

University of Illinois Urbana-Champaign

伊利诺伊香槟 · Siebel School of Computing & Data Science · 公立校 AI 研究强校
USNews CS #5
①

AI program 核心专业课 & Listed Faculty

MS CS · MCS · 新 Siebel 学院

UIUC 2024 年成立 Siebel School of Computing and Data Science（合并 CS + Stat 部分）。MS CS 学生可选 AI 作为 core area —— UIUC 在该 area 下提供的课列得非常全。

CS 440Artificial Intelligence
CS 441Applied Machine Learning
CS 442Trustworthy Machine Learning
CS 443Reinforcement Learning
CS 444Deep Learning for Computer Vision
CS 445Computational Photography
CS 446Machine Learning
CS 447Natural Language Processing
CS 448Audio Computing Laboratory
CS 540Visual Recognition
CS 542Statistical Reinforcement Learning
CS 543Computer Vision
CS 544Optimization in Computer Vision
CS 545Machine Learning for Signal Processing
CS 546Advanced Topics in NLP
CS 588Autonomous Vehicle System Engineering
CS 598Deep Learning for Healthcare

Listed Faculty（Siebel/CS AI 方向，节选）：

Jiawei Han Heng Ji Tarek Abdelzaher Saurabh Gupta Svetlana Lazebnik David Forsyth Yuxiong Wang ChengXiang Zhai Julia Hockenmaier Hanghang Tong
②

非 CS 系硕士生选 AI 课的政策

先修自动检查 · Siebel 内部更易
A · 硬性门槛

核心规则：UIUC 通过 Banner 系统自动强制 prerequisite——所有 CS 440-448、CS 540-546 等 AI 主干课都在强制名单上。如果系统检测到非 CS 学生没有 CS 225 等先修课，会直接报错"Course Prerequisite and/or Test Score Error – Contact Course Department"。

非 CS 系硕士生想选 CS AI 课的两步：

(1) 通过 prerequisite check（如已修等价 ML 课、概率统计、Python）→ 让 home department advisor 帮忙发 registration permission override 给 CS 系。
(2) Siebel CS 内部对热门课有 capacity reserve："Students in CS majors have specific sections to register for"——CS 124、128、173、225、233 这种基础课对非 CS 学生开放但有特定 section。AI 研究生课（CS 440 系列）没有 reserve seating，但 capacity 紧张时优先 CS master。

2024 年 Siebel School 重大变化：原 CS 系 + 部分 Stat 系合并为 Siebel School of Computing & Data Science——这意味着 Siebel 内的 master 学生（含 MS CS、MS Stat 等）跨系选 AI 课摩擦显著降低，因为同属一个学院。

B · 学位计算

UIUC 有两个非常友好的非 CS 入口硕士：MCS（Master of Computer Science）和 MCS-DS（Data Science，在线/在校都有）——后者直接把 CS + Stat + iSchool（Information Sciences）的课程整合在一个 degree。MCS-DS 的 8 门课中至少 4 门是 ML/AI 方向（如 CS 410 Text Info Systems、CS 411 Database、CS 412 Data Mining、CS 446 ML）。

MS Stat、MS Math、Gies MSBA 各自的 program guide 列出可计 elective 的 CS 课，但要注意不同硕士有不同上限（通常每个 elective bucket 限 2-3 门）。

来源：siebelschool.illinois.edu/academics/undergraduate/registration/cs-course-restrictions-enrollment-caps · advising.grainger.illinois.edu/course-registration/prerequisite-enforcement · registrar.illinois.edu/registration/registration-process/enrollment-reqs-prereqs · siebelschool.illinois.edu/academics/graduate/ms-program
③

与 AI 交叉的硕士项目（6 领域）

UIUC × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MS in Statistics（含 Statistical Computing & Data Analytics 方向）
MS in Statistics – Analytics Concentration
2024 Siebel School 合并后，Stat 部分课程已被并入 Siebel
2024 Siebel 整合后边界模糊
Stat #16
USNews
 强
CS 446 计 Stat MS≈ 65%
 强
Siebel School 联合
课程重合详情
CS AI 课与 Stat MS 的重合
全部 重合 elective 独有
课号课程类型
CS 446 / IE 534Machine Learning重合（cross-listed）
CS 542Statistical Reinforcement Learning重合
STAT 542Statistical Learning重合（与 CS 446 等价）
STAT 432Basics of Statistical Learning重合
CS 540Reasoning Under Uncertainty重合
CS 444Deep Learning for Computer Visionelective
CS 447Natural Language Processingelective
CS 543Computer Visionelective
STAT 425Applied Regression & Design仅 Stat
STAT 510Mathematical Statistics II仅 Stat

GTech 生物 AI 主体 = Wallace H. Coulter Department of Biomedical Engineering (joint Georgia Tech & Emory, 全美 BME 排名常年第一) + School of Biological Sciences + School of Computational Science and Engineering (CSE)（独立 School in College of Computing）。开 MS in Bioinformatics（跨 BIO + Coulter BME + CoC 三院）+ MS in CSE (Computational Bioscience track)。Borodovsky (Regents) 是 GeneMark 创始者; Skolnick (GRA Eminent + Gibson Chair) 主导 Systems Biology; Weitz (Patton Chair + Blaise Pascal Chair) 桥接 ML × 生态学。

师资重合详情
Siebel 中的 Stat × ML 教师
全部 joint primary affiliated
姓名主要方向关系
Yuguo ChenDepartment Chair + Professor of Stat; ASA Fellow; Monte Carlo methods + network data + bioinformatics + state space modelsStat Chair, ASA Fellow
Tong ZhangProfessor of Stat; 2024 ICML Test of Time Award winner (deep learning); former HKUST + Rutgers; large-scale ML + deep learning theory + non-convex optimization; AAAI Fellow + ASA FellowStat primary, AAAI Fellow
Feng LiangProfessor of Stat; Bayesian methods + decision theory + score-based diffusion model theory (PMLR 2024); minimum description length + data miningStat primary
Naveen Naidu NarisettyProfessor of Stat; high-dim data + Bayesian computation + functional data + IGB Computational Genomics affiliateStat + IGB
Sihai Dave ZhaoAssociate Professor Stat + Bioengineering + Biomedical/Translational Sciences + Assistant Director of Computational Genomics IHSI + IGB Affiliate; statistical genomics + high-dim statisticsStat + BioE + IGB Computational Genomics
Ruoqing ZhuPhD Program Director + Associate Professor Stat; tree-based methods (random forests) + personalized medicine + sufficient dimension reduction; ASA Statistical Learning & DS Section serviceStat PhD Director
Sabyasachi ChatterjeeAssociate Professor Stat; high-dim statistics + shape constrained inference + signal estimationStat primary
Steven Andrew CulpepperProfessor Stat; statistical/mathematical methods + ML & data mining + signal processing + precision medicineStat primary
Yun YangAdjunct Associate Professor Stat; ML + scalable Bayes inference + theoretical foundations of high-dim problemsStat Adjunct
Daniel J EckAssistant Professor Stat; predictive inference + sports analytics + ML evaluationStat primary
Eren C KızıldağAssistant Professor Stat; statistical learning theory + high-dim probability + algorithmic phase transitionsStat primary
Jingbo LiuAssistant Professor Stat; information theory + ML + statistical inference + high-dim probabilityStat primary
Joshua S AgterbergAssistant Professor Stat; high-dim statistics + network data + tensor methods + ML theoryStat primary
Shulei WangAssistant Professor Stat; microbiome data analysis + high-dim statistics + statistical machine learning; IGB Computational Genomics affiliateStat + IGB
Yuexi WangAssistant Professor Stat; simulation-based inference + Bayesian computation + MLStat primary
Lili ZhengAssistant Professor Stat; high-dim statistics + tensor learning + network analysisStat primary
Yuchen ZhouAssistant Professor Stat; high-dim statistics + tensor methodsStat primary
Susu ZhangAssociate Professor Stat; educational measurement + ML for psychometricsStat primary
Tandy WarnowAffiliate Professor Stat + Founder Professor of Computer Science + Grainger Distinguished Chair in Engineering; NAS Member; phylogenomics + multiple sequence alignment + ML for biologyCS + Stat Affiliate, NAS
Chengxiang ZhaiAffiliate Professor Stat + Donald Biggar Willett Professor of Engineering + Professor of CS; NLP + information retrieval + ML; ACM Fellow + AAAS FellowCS + Stat Affiliate, ACM Fellow
Olgica MilenkovicAffiliate Professor Stat + Donald Biggar Willett Professor of ECE; IEEE Fellow; gene regulatory networks + compression + ML for genomicsECE + Stat Affiliate, IEEE Fellow
Zhizhen ZhaoAffiliate Professor Stat + Associate Professor ECE; ML for imaging + manifold learning + NSF CAREERECE + Stat Affiliate
Venugopal VeeravalliAffiliate Professor Stat + Henry Magnuski Professor ECE; IEEE Fellow; statistical signal processing + MLECE + Stat Affiliate, IEEE Fellow
Bo LiAdjunct Professor Stat + Professor of CS; spatio-temporal modeling + Bayesian hierarchical for climatology + public healthStat + CS Adjunct
Xiaofeng ShaoAdjunct Professor Stat (now at WashU); time series + high-dim inferenceStat Adjunct
Xiaohui ChenAdjunct Associate Professor Stat; high-dim statistics + MLStat Adjunct
Math
数学
MS in Mathematics
MS in Applied Mathematics
注：UIUC 没有专门的 AI×Math 项目，但课程开放
Math MS 主要研究型
Math #25
USNews
 中
靠 elective≈ 45%
 中
部分应用方向重合
课程重合详情
Math 系与 CS AI 课程的交叉
全部 重合 elective 独有
课号课程类型
MATH 595Special Topics（含 Topology of Data 等 ML 方向）重合（按学期）
CS 446Machine Learningelective
STAT 542Statistical Learningelective
CS 540Reasoning Under Uncertaintyelective
MATH 540Real Analysis仅 Math
MATH 525General Topology仅 Math

GTech School of Chemistry & Biochemistry 公开 "theoretical chemistry core" 包括 ML in 化学方向, 系内 David Sherrill (Regents) 是 Psi4 开源量子化学软件主要开发者, 系列出 Sherrill, Gumbart, Kretchmer, McDaniel 为 ML/computational chem 旗舰。School of Chemical and Biomolecular Engineering (ChBE) 由 Hang Lu (Wang Chair) 任 School Chair, ML 方向强势。Brédas (NAE) 是有机半导体 + 材料 ML 国际领导者。

师资重合详情
Math 系做 ML 的教师
全部 joint affiliated
姓名主要方向关系
Lee DeVilleProfessor School of Math; stochastics + applied probability + mathematical biology + dynamical systems for MLMath primary
Yuliy BaryshnikovProfessor School of Math + ECE; stochastics + computational geometry/topology for MLMath + ECE
Anil HiraniAssociate Professor School of Math + CS; numerical analysis + computational geometry + topology + ML for PDEsMath + CS
Tandy WarnowFounder Professor of CS + Grainger Distinguished Chair in Engineering; NAS Member; algorithmic ML for biology + phylogenomicsCS + Math, NAS
Richard SowersProfessor School of Math + ISE + Stat Affiliate; stochastic analysis + financial math + ML for transportationMath + ISE + Stat
Kay KirkpatrickProfessor School of Math; mathematical physics + probability + ML applicationsMath primary
Zhizhen ZhaoAssociate Professor ECE + Stat Affiliate; NSF CAREER; manifold learning + ML for imaging + numerical analysisECE + Math affiliated
Marius JungeProfessor School of Math; quantum information theory + IQUIST affiliatedMath + IQUIST
Felix LeditzkyAssistant Professor School of Math; quantum information theoryMath primary
Nathan DunfieldProfessor School of Math; computational topology + ML applicationsMath primary
Carla CáceresProfessor School of Integrative Biology; mathematical biology (Math affiliate)SIB + Math affiliated
Jun SongProfessor of Physics + IGB; integrative genomics + physics-inspired machine learning; gene regulation + cancer genomicsPhysics + IGB
Bio
生物
MS in Bioinformatics（CS + Crop Sciences + Biology 联合）
MS in Computational Science & Engineering（CSE）
MEng in Bioengineering
Bioinformatics 项目历史悠久
Bioinformatics Top 15
估算
 强
CSE 含 ML 主线≈ 65%
 强
IGB 研究院共聘
课程重合详情
Bioinformatics MS 与 CS AI 的重合
全部 重合 elective 独有
课号课程类型
CS 466 / IB 534Introduction to Bioinformatics重合
CS 466Computational Biology重合
CS 446Machine Learning重合（Bioinformatics MS elective）
STAT 558Statistical Computing重合
CS 444Deep Learning for CV（用于医学影像）elective
IB 533Computational Genomics重合
IB 411Cell & Developmental Biology仅 Bio
IB 510Quantitative Plant Pathology仅 Bio

GTech School of Physics 公开 "Artificial Intelligence and Machine Learning" 为系级研究领域, 自述: "designing machine learning and reasoning algorithms to accelerate physics research, as well as using techniques from physics to develop reliable AI algorithms"。Daniel Goldman (NAS, Dunn Chair, CRAB Co-Director) 是 active matter + 物理-机器人 ML 全球开创者; Fenton + Cvitanović 在 nonlinear dynamics + ML 方向有深厚积累。物理 master 走 MS in Physics + 跨 CSE。

师资重合详情
Bio × ML 教师
全部 joint primary affiliated
姓名主要方向关系
Saurabh SinhaFounder Professor + Professor of CS + Bioengineering + IGB Computational Genomics theme; regulatory genomics + systems biology + neurogenomics + cancer genomicsCS + BioE + IGB
Huimin ZhaoSteven L. Miller Chair in Chemical and Biomolecular Engineering + Professor of Chemistry + BioE + IGB; NAE Member + ASEE Fellow + AAAS Fellow; synthetic biology + ML + lab automation; co-leader Molecule Maker Lab Institute (NSF AI Institute)ChBE + Chem + BioE + IGB, Miller Chair, NAE
Tandy WarnowFounder Professor + Grainger Distinguished Chair in Engineering + Professor CS; NAS Member; phylogenomics + multiple sequence alignment + metagenomics MLCS, NAS, Grainger Chair
Jimeng SunHealth Innovation Professor + Professor of CS + Carle Illinois College of Medicine; data & information systems + bioinformatics + AI for healthcare; LLM for medical reasoningCS + Carle Med, Health Innovation Prof
Sergei MaslovBliss Faculty Scholar + Professor of Bioengineering + Physics + IGB; bioinformatics + biomolecular modeling + cancer genomicsBioE + Physics + IGB, Bliss Scholar
Olgica MilenkovicDonald Biggar Willett Professor of ECE; IEEE Fellow; gene regulatory networks + DNA storage + compression for genomicsECE, IEEE Fellow
Sihai Dave ZhaoAssociate Professor Stat + BioE + Biomedical/Translational Sciences + Assistant Director of Computational Genomics IHSI; statistical genomics + ML for biomarkerStat + BioE + IHSI
Jun SongProfessor of Physics + IGB; integrative genomics + physics-inspired machine learning + chromatin structure + cancer genomicsPhysics + IGB
Halil KilicogluAssociate Professor School of Information Sciences (iSchool); biomedical informatics + NLP + literature-based knowledge discoveryiSchool primary
Paul JensenAssociate Professor Bioengineering; infectious diseases + accelerating microbiology + ML for drug discovery (JensenLab)BioE primary
Rohit BhargavaFounder Professor + Professor of Bioengineering + Cancer Center at Illinois Director; NAI Fellow + AAAS Fellow; AI for cancer pathology + biomedical imagingBioE Cancer Center Director, NAI Fellow
Yang LiuAssistant Professor Bioengineering; ML for nanopore sequencing + genomicsBioE primary
Mark AnastasioDonald Biggar Willett Professor in Engineering + BioE Department Head; IEEE Fellow + AIMBE Fellow + SPIE Fellow; AI for medical imagingBioE Head, IEEE+AIMBE+SPIE Fellow
Chem
化学
MS in Chemistry（罕见，主要 PhD）
MS in Materials Science & Engineering（含 ML for Materials）
无独立 AI×Chem 项目
Chem MS 极少
Chem Top 10
USNews
 弱
仅个别课< 25%
 中
MRL 联合
课程重合详情
Chem/MSE 中 AI 元素课程
全部 重合 elective 独有
课号课程类型
MSE 555Computational Materials Methods重合
CHEM 580Quantum Mechanics仅 Chem
CS 446Machine Learningelective
CSE 555Special Topics in CSE（含 ML for Sci）elective

GTech 商科 AI 集群: (1) Scheller College of Business 2026-2 月将原 Business Analytics Center 升级为 Center for AI in Business, 由 Adithya Pattabhiramaiah (Pearce Chair, Marketing) 任 inaugural Faculty Director; (2) MS in Analytics (interdisciplinary)——U.S. News 公立 #1 MBA Business Analytics 4 年蝉联, 跨 Scheller + College of Computing + College of Engineering 三院; (3) MS in Quantitative and Computational Finance (MS QCF) 由 Math + ISyE + Scheller 三院联办（top 10 北美）, Sudheer Chava 任 director; (4) ISyE MS in Analytics 互通; (5) Scheller 教 "Machine Learning for Business" + "AI for Business" 等独立 AI 课。

师资重合详情
Chem × ML 教师
全部 affiliated
姓名主要方向关系
Martin D. BurkeMay and Ving Lee Professor for Chemical Innovation + Professor of Chemistry + Carle Illinois College of Medicine; HHMI Investigator; Director Molecule Maker Lab Institute (NSF AI Institute, $20M, AI for chemistry automation); AI + automated synthesis (Science 2022, Nature 2024)Chem + Carle Med, MMLI Director, HHMI
Huimin ZhaoSteven L. Miller Chair in ChBE + Professor of Chem + BioE + IGB; NAE Member; ML + synthetic biology + automated DBTL pathway engineering; co-leader MMLIChBE + Chem + BioE, NAE, Miller Chair
Nicholas E. JacksonAssistant Professor of Chemistry; 2021 Camille and Henry Dreyfus Foundation Machine Learning in Chemical Sciences Award; ML for soft matter + organic electronicsChem primary, Dreyfus ML Award
Scott E. DenmarkReynold C. Fuson Professor of Chemistry; NAS Member; ML-driven catalyst design + computational chemistry; co-leader MMLIChem, Fuson Chair, NAS
So HirataMarvin T. Schmidt Professor of Chemistry; theoretical chemistry + electronic structure + ML for ab initio methodsChem, Schmidt Chair
Ying DiaoAssociate Professor ChBE + co-leader MMLI; ML for soft electronics + polymer crystallizationChBE + MMLI
Charles M. SchroederJames Economy Professor of Materials Science and Engineering; ML for polymer dynamics + soft materials; collaborator in MMLIMSE + MMLI, Economy Chair
Christy F. LandesJerry A. Walker Endowed Chair in Chemistry; AAAS Fellow + APS Fellow; ML for super-resolution microscopy + single-molecule analysisChem, Walker Chair, AAAS Fellow
Phys
物理
MS in Physics（罕见）
MS in Computational Science & Engineering（CSE，可作为转 AI 入口）
NCSA AI 计划
CSE 是转型路径
Phys Top 10
USNews
 中
CSE 接受 Phys 背景≈ 40%
 中
NCSA 联合
课程重合详情
Phys 涉及的 AI 课
全部 重合 elective 独有
课号课程类型
CSE 411Numerical Analysis（CSE 必修）重合
PHYS 466Atomic Scale Simulations重合
CS 446Machine Learningelective
PHYS 580Quantum Mechanics I仅 Phys

UIUC Department of Statistics（in LAS, Computing Applications Building）开 MS in Statistics + MS Stat with Concentration in Analytics。系所与 Carl R. Woese Institute for Genomic Biology (IGB) + Siebel School of Computing and Data Science (新名, 2024-) + NCSA + ECE形成 web。Tong Zhang (AAAI Fellow, 2024 ICML Test of Time Award) 是大规模 ML 国际顶级理论家。Tandy Warnow (NAS) 是计算系生物方向 NAS 院士 (Stat affiliate)。Chengxiang Zhai (ACM Fellow) NLP/IR 国际权威 (Stat affiliate)。

师资重合详情
Physics × ML 教师
全部 joint
姓名主要方向关系
Jun SongProfessor of Physics + Carl R. Woese Institute for Genomic Biology; integrative genomics + physics-inspired machine learning + cancer genomics + chromatin structurePhysics + IGB
Aleksei AksimentievProfessor of Physics + Center for the Physics of Living Cells; NAS Member of Polish Academy; ML + molecular dynamics for nanopore biophysics; (Nature Biotech 2020, Nature Nanotech 2019)Physics + CPLC
Bryan ClarkAssociate Professor of Physics; quantum many-body simulation + ML for condensed matter + tensor networks + neural network wavefunctionsPhysics primary
Eduardo FradkinDonald Biggar Willett Professor in Physics + ICMT Director; NAS Member + APS Fellow; condensed matter theory + quantum many-body + ML applicationsPhysics, ICMT Director, NAS
Nigel GoldenfeldSwanlund Endowed Chair Emeritus + Professor of Physics (now UCSD); NAS Member; statistical physics + biology + universal scaling + ML for complex systemsPhysics, NAS
Gautham NarayanAssistant Professor of Astronomy + NCSA Senior Research Scientist; ML for transient astronomy + LSST/Rubin Observatory; co-author Cosmology ML review (2022)Astronomy + NCSA
Vidya MadhavanDonald Biggar Willett Professor of Physics; STM + ML for topological materials; APS FellowPhysics, Willett Chair
Smitha VishveshwaraProfessor of Physics; quantum many-body + condensed matter + ML applicationsPhysics primary
Gilbert HolderProfessor of Physics + Astronomy; cosmology + CMB + ML for parameter inferencePhysics + Astronomy
Biz
商科
MS in Business Analytics (Gies)
MS in Financial Engineering
MSAI 是 Gies 与 Siebel 合作的新项目
Gies MSBA 跨界设计
Gies #19 / Online MBA #1
USNews
 中
MSBA 含 ML≈ 50%
 中
Gies + Siebel 合作
课程重合详情
Gies MSBA 中的 AI 课
全部 重合 elective 独有
课号课程类型
BADM 555Predictive Analytics重合
BADM 567Process Managementelective
CS 446Machine Learningelective（需 advisor 批准）
BADM 587Big Data Analytics with Hadoop重合
FIN 580Financial Engineering仅 MSF
STAT 542Statistical Learningelective

UIUC Math 系开 MS in Math + MS Math with Concentration in Actuarial Science + MS in Applied Math。AI/ML 主要桥梁通过 DEAM (Differential Equations and Applied Math) group: DeVille, Baryshnikov, Hirani, Sowers, Kirkpatrick + 跨 ECE 的 Zhizhen Zhao。数学背景 master 也常通过新设的 Siebel School of Computing and Data Science (2024 接受 $50M+ Siebel 捐款重命名)开 MCS Data Science (online + on-campus) 进入 AI master 路径。

师资重合详情
Gies × Siebel 教师
全部 joint primary affiliated
姓名主要方向关系
Gautam PantProfessor of Business Administration + Director MS Business Analytics; machine learning + data science for firm operations + competitor detectionGies BA Director
Sridhar SeshadriCharles and Joan Suever Professor + Department Head Business Administration; INFORMS Fellow; supply chain + data analytics for operationsGies Dept Head, Suever Chair
Aric RindfleischJohn M. Jones Professor of Marketing + Executive Director Illinois MakerLab; AI/ML × consumer behavior + 3D printing innovationGies Mktg, Jones Chair
Ramji BalakrishnanDistinguished Professor of Accountancy + Department Head; managerial accounting + analytics for cost systemsGies Accy Head
Madhu ViswanathanDiane and Steven N. Miller Professor of Marketing; marketplace literacy + subsistence consumer analytics + ML for social goodGies Mktg, Miller Chair
Shashank RaoProfessor of Supply Chain Management; ML for logistics + B2B analyticsGies SCM
Jonathan AronsonLecturer + Director MSF + Master in Finance + executive ed; AI in Finance + fintechGies Finance director
Vishal SachdevClinical Associate Professor of Business Administration + Director Illinois MakerLab; AI for digital business + maker movementGies BA primary
Eric LarsonClinical Associate Professor of Information Systems; ML for product analytics + retail analyticsGies IS primary
Yili HongProfessor of Information Systems; ML × digital platforms + crowdsourcing analyticsGies IS primary

UIUC 在公立大校中课程量最丰富（CS 440-448 这一系列就是 8 门完整 AI 主干课），且 Siebel 新学院整合后跨 Stat/CS 的边界变得更模糊。但跨学科硕士项目数量比 GTech 少，理工科转型主要靠 MS CS / MCS / MCS-DS / MS Stat 这几个入口。转型最佳路径：Stat/Math 背景 → MS Stat-DS；非 CS 但已有编程基础 → MCS-DS（在线，无背景限制）；商科 → Gies MSBA。

来源：siebelschool.illinois.edu · stat.illinois.edu · giesbusiness.illinois.edu
07

Cornell University

康奈尔 · Bowers College of CIS · 2024 年新设 AI Minor · Cornell Tech NYC 校区主推 AI 应用
USNews CS #6
①

AI program 核心专业课 & Listed Faculty

MEng CS · MS CS · Cornell Tech

Cornell 2024 年从 CS 系扩展出独立的 AI Minor（伴随的是把 CS 4780 改名为 CS 3780，作为入门 ML 课）。研究生层面通过 MEng / MS in CS 提供 AI 方向。Cornell Tech 在纽约的 1 年 Master of Engineering in CS 也含 ML 方向。

CS 4700Foundations of AI
CS 3780/5780Introduction to Machine Learning
CS 4740Natural Language Processing
CS 4742Foundations of Reasoning
CS 4750Foundations of Robotics
CS 4775Computational Genetics & Genomics
CS 4782Deep Learning（new）
CS 6740Advanced Language Technologies
CS 6741NLP and ML Topics
CS 6742NLP and Social Interaction
CS 6756Learning for Robot Decision Making
CS 6766Reasoning about Uncertainty
CS 6780Advanced Machine Learning
CS 6781Theoretical Foundations of ML
CS 6783Machine Learning Theory
CS 6784Advanced Topics in ML
CS 6785Deep Probabilistic & Generative Models
CS 6787Advanced ML Systems
CS 6789Theoretical Foundations of RL
ORIE 4741/5741Learning with Big Messy Data

Listed Faculty（CIS AI/ML 核心，节选）：

Thorsten Joachims Kilian Weinberger Claire Cardie Lillian Lee Bart Selman Carla Gomes Jon Kleinberg Karthik Sridharan Volodymyr Kuleshov Yoav Artzi Christopher De Sa Anil Damle
②

非 CS 系硕士生选 AI 课的政策

明文限制 + 部门间课程等价
A · 硬性门槛

核心规则：Cornell 的 CS 主干 ML 课明文限制——CS 5780 / CS 4780 的 class roster 写："Pre-enrollment is limited to CS majors; others can waitlist during Add/Drop"（2025-2026）。CS 6780 Advanced ML 等研究生级课程也类似——非 CIS 学生只能 waitlist。

但 Cornell 用一种非常聪明的方式解决了这个问题：把 CS 3780/5780 与 ECE 3200/5420、ORIE 3741/5741、STSCI 3740/5740 列为"Forbidden Overlaps"——意思是这些课内容等价，互不可重复算学分。这就让 ECE / ORIE / Statistics 系的 master 生能在自己系修等价的 ML 入门课，无需挤进 CS 5780。这是 Top 8 中"课程互认"做得最系统的。

对于 CS 5780 之后的高级 AI 研究生课（CS 6740 Advanced Language Tech、CS 6766 Reasoning about Uncertainty、CS 6785 Deep Generative Models 等），普遍写"The course is open to master students with instructor permission"——即非 CS master 需要 instructor 许可，但门是开的。

B · 学位计算

Cornell Tech 的 Master in CS（focused on ML/AI）是 1 年项目，专为转型设计，外系背景可申请；Cornell 主校的 MPS in CIS也接受非 CS 背景。MPS in Applied Statistics、MPS in ORIE各自的 program of study 都明文列入 CS AI 课作为认可 elective——比如 ORIE master 可以把 CS 6780 算入 elective。

非 CIS master 想把 CS 课算入 degree：先获 instructor permission 入学，再让 home dept 的 graduate field assistant 在 plan of study 里批准。整体而言Cornell 的"反向通道"（外系→AI）比"正面通道"（外系直接选 CS 5780）顺畅得多。

来源：classes.cornell.edu/browse/roster/FA25/subject/CS · courses.cornell.edu/courses/cs · cs.cornell.edu/courses/cs6741 · tech.cornell.edu
③

与 AI 交叉的硕士项目（6 领域）

Cornell × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MPS in Applied Statistics
MS in Statistics（PhD 中途授予）
MS in Operations Research with Statistics Track
MPS Applied Stat 1 年项目
Stat #10
USNews
 强
明文 Forbidden Overlaps≈ 70%
 强
STSCI + CS 联合
课程重合详情
CS / ECE / ORIE / STSCI 跨系等价 ML 课
全部 重合 等价 elective 独有
课号课程类型
CS 5780Machine Learning（CS 限定）重合（限 CS major）
STSCI 5740Data Mining & Machine Learning等价（Forbidden Overlap with CS 5780）
ORIE 5741Learning with Big Messy Data等价（Forbidden Overlap with CS 5780）
STSCI 5740Data Mining & ML重合
STSCI 5045Numerical Linear Algebra（应用于 ML）重合
STSCI 5060Stat Computing重合
CS 6740Advanced Language Techelective（master 需 instructor permission）
CS 6780Advanced Machine Learningelective
STSCI 4520Statistical Computing仅 Stat
STSCI 5080Probability Theory仅 Stat

UIUC 生物 AI 主体 = Carl R. Woese Institute for Genomic Biology (IGB)（cross-departmental 大研究所）+ Department of Bioengineering（Engineering College, Mark Anastasio 任 Head）+ Carle Illinois College of Medicine（全美首个工程主导的 MD program）+ Cancer Center at Illinois（Bhargava 任 Director）。IGB Computational Genomics theme明确列出 ~12 位 PI 含 AI/ML 关键词。 Huimin Zhao (NAE, Miller Chair) 是 NSF AI Institute "Molecule Maker Lab Institute" 联合创始人。Master 路径: MS Bioinformatics + MS Bioengineering + MS Stat。

师资重合详情
Statistics & Data Sciences + CS 共聘
全部 joint primary
姓名主要方向关系
Florentina BuneaProfessor + Director of Graduate Studies, Stat & DS; IMS Fellow + ASA Fellow; high-dim statistics + topic models + machine learning; ML faculty at Stat dept ML clusterStat&DS DGS, IMS+ASA Fellow
Marten WegkampProfessor of Math + Stat & DS; IMS Fellow; high-dim statistics + statistical learning theory + empirical processMath + Stat&DS, IMS Fellow
Martin T. WellsCharles A. Alexander Professor of Statistical Sciences + Professor at Weill Cornell Medical College + Director of Research, ILR + ASA Fellow + IMS Fellow; high-dim + Bayesian + causal + MLStat&DS + WCM, Alexander Chair
David RuppertAndrew Schultz Jr. Professor of Engineering ORIE + Stat & DS; NAS Member + ASA Fellow + IMS Fellow; nonparametric + functional data + MLORIE + Stat&DS, NAS, Schultz Chair
Sumanta BasuAssociate Professor Stat & DS; high-dim time series + network ML + statistical genomicsStat&DS primary
Ahmed El AlaouiAssistant Professor Stat & DS; high-dim probability + statistical-computational tradeoffs in ML + algorithmic phase transitionsStat&DS primary
Ziv GoldfeldAssociate Professor ECE + Stat & DS field faculty; information theory for ML + generative models + optimal transport ML; NSF CAREERECE + Stat&DS
Kengo KatoProfessor Stat & DS; high-dim statistics + functional data + ML theoryStat&DS primary
Yang NingProfessor Stat & DS; high-dim inference + ML + optimization; ASA + IMS activeStat&DS primary
Y. Samuel WangAssistant Professor Stat & DS; high-dim statistics + causal inference + MLStat&DS primary
Dana YangAssistant Professor Stat & DS; high-dim statistics + ML theory + statistical-computational gapsStat&DS primary
David MattesonAssociate Department Chair + Professor Stat & DS; change-point detection + time series + functional data + MLStat&DS Assoc Chair
Yuguo ChenAffiliate Professor Stat & DS (primary at UIUC Stat); Monte Carlo + bioinformatics + MLStat&DS Affiliate
Jacob BienAffiliate Professor Stat & DS (primary at USC Marshall); high-dim statistics + sparse modeling + MLStat&DS Affiliate
James BoothProfessor Stat & DS; statistical genomics + functional data + bioinformaticsStat&DS primary
Karthik SridharanAssociate Professor of CS + Stat & DS field faculty; statistical learning theory + online learning + ML foundationsCS + Stat&DS field
Thorsten JoachimsProfessor of CS + Stat & DS field faculty; ACM Fellow + ASA + IMS; ML + IR + ranking + counterfactual learningCS + Stat&DS field, ACM Fellow
Kilian Q. WeinbergerProfessor of CS + Stat & DS field faculty + Director TRIPODS Center for Data Science; deep learning + ML for graphs + ML for sciencesCS + Stat&DS field, TRIPODS Director
Amy KuceyeskiAssociate Professor Radiology Weill Cornell + Stat & DS field faculty + Director Computational Connectomics Lab; ML for neuroimaging + brain connectivityWCM Radiology + Stat&DS field
Nathan KallusAssociate Professor ORIE + Cornell Tech + Stat & DS field faculty; causal inference + ML + RL + algorithmic fairness + banditsORIE Cornell Tech + Stat&DS field
Peter FrazierEleanor and Howard Morgan Professor of Engineering ORIE; Bayesian optimization + ML for experimental design; INFORMS awardORIE Morgan Chair, INFORMS
Christina Lee YuAssistant Professor ORIE + Stat & DS field faculty; causal inference + reinforcement learning + matrix completionORIE + Stat&DS field
Siddhartha BanerjeeAssociate Professor ORIE; RL + algorithmic game theory + online learning + networksORIE primary
Olgica MilenkovicAffiliate (primary at UIUC ECE)Affiliate
Tom DiCiccioProfessor Stat & DS; bootstrap + likelihood inference + asymptotic statisticsStat&DS primary
Felix ThoemmesProfessor Stat & DS + Human Development; causal inference + missing dataStat&DS + Human Dev
Math
数学
MS in Mathematics（罕见）
MEng in ORIE（运筹工程，最直接的数学→AI 路径）
无独立 Applied Math MS
ORIE MEng 是数学背景核心入口
Math Top 15
USNews
 中
ORIE 含 ML≈ 50%
 中
部分共聘
课程重合详情
Math/ORIE 中含 AI 的课
全部 重合 elective 独有
课号课程类型
ORIE 5741Learning with Big Messy Data（ML 入门）重合
ORIE 4525Statistical Modeling & Data Analytics重合
ORIE 5380Optimization Methods重合
CS 5780Machine Learning（限 CS major）elective（外系 waitlist）
MATH 7110Algebra仅 Math
MATH 7330Topology仅 Math

UIUC Chemistry + ChBE 在化学 AI 方向集结于 Molecule Maker Lab Institute (MMLI)——NSF 2020 资助 5 年 $20M 的国家级 AI Institute, by Martin Burke (Director) + Scott Denmark + Ying Diao + Jiawei Han (CS) + Huimin Zhao。MMLI 的 Closed-Loop Transfer 工作发表 Science 2022 + Nature 2024。Burke (HHMI Investigator) + Denmark (NAS, Fuson Chair) + Zhao (NAE, Miller Chair) 共三位顶级 endowed chair 同时在 MMLI。

师资重合详情
数学 × ML 教师
全部 joint primary affiliated
姓名主要方向关系
David BindelProfessor of CS + Director Center for Applied Math (CAM) + SIAM Fellow + Sloan Fellow; numerical linear algebra + kernel methods + spectral network analysis + scientific MLCS + CAM Director, SIAM+Sloan Fellow
Alex TownsendAssociate Professor of Math; SIAM Fellow + Sloan Fellow; numerical analysis + scientific ML + low-rank approximation + neural networks for PDEsMath, SIAM+Sloan Fellow
Anil DamleAssociate Professor of CS; numerical linear algebra + computational quantum chemistry + spectral clustering + ML algorithmsCS + Math affiliated
Yunan YangAssistant Professor of Math; optimal transport + inverse problems + ML for PDEsMath primary
Marten WegkampProfessor of Math + Stat & DS; IMS Fellow; high-dim statistics + statistical learning theory; ML field facultyMath + Stat&DS, IMS Fellow
Christopher EarlsProfessor of CEE + CAM Field Faculty; scientific machine learning + computational mechanics + data-driven modelingCEE + CAM
Lionel LevineProfessor of Math; combinatorics + probability + algorithmic game theoryMath primary
Steven StrogatzSusan and Barton Winokur Distinguished Professor in the Life Sciences (Math); nonlinear dynamics + complex networks + collective behavior; popular author of math booksMath, Winokur Chair
Karola MészárosProfessor of Math; combinatorics + algebra + probabilistic methodsMath primary
Alex VladimirskyProfessor of Math; numerical analysis + control theory + optimization + computational PDEsMath primary
Robert ConnellyProfessor Emeritus of Math; computational geometry + tensegrityMath Emeritus
Florian FrickAssociate Professor of Math; combinatorics + algebraic topology + applied topology for MLMath primary
Bio
生物
MS in Computational Biology
Tri-Institutional CompBio PhD（与 Memorial Sloan Kettering + Rockefeller 合作）
MS in Animal Science with Bioinformatics
Tri-Institutional 与纽约市顶级机构联合
Bio Top 5
USNews
 强
CompBio + ML 主线≈ 65%
 强
Cornell+MSK+Rockefeller
课程重合详情
CompBio MS / 与 CS AI 的重合
全部 重合 elective 独有
课号课程类型
CS 4775 / 5775Computational Biology重合（cross-listed undergrad+grad）
BIOMI 4090Computational Genomics重合
CS 6780Advanced Machine Learning（CompBio 应用）elective
CS 5784Reinforcement Learning（药物发现）elective
BIOMG 6310Molecular Biology仅 Bio
BIOMG 7430Genome Biology仅 Bio

UIUC Physics 系（45 senate faculty）AI/ML 主要集中在: (1) condensed matter + quantum many-body（Bryan Clark 神经网络波函数, Madhavan ML 拓扑材料, Fradkin 凝聚态理论 NAS）; (2) biophysics（Aksimentiev nanopore + 分子动力学 ML）; (3) cosmology + astrophysics（NCSA + LSST 集群, Narayan ML transient）; (4) genomics-physics 跨界（Jun Song 物理启发 ML × 染色质）。NCSA (National Center for Supercomputing Applications) 是全美超算中心之一, 内设 Center for Artificial Intelligence Innovation (CAII)。

师资重合详情
Cornell × MSK × Rockefeller 联合
全部 joint affiliated
姓名主要方向关系
Adam SiepelField Faculty Computational Biology + Adjunct Professor (primary at CSHL); statistical & population genomics + ML for evolutionComp Bio Field + CSHL
Jason MezeyAssociate Professor + Genetics, Genomics & Development + WCM; statistical genetics + computational biology + pathway modeling + statistical MLCompBio + WCM
Amy KuceyeskiAssociate Professor of Radiology Weill Cornell Medical College + Founder Machine Learning in Medicine working group + Computational Connectomics Lab Director; ML + neuroimagingWCM Radiology + CompBio
Andrew G. ClarkJacob Gould Schurman Professor of Population Genetics + MBG; NAS Member + AAAS Fellow; gene regulatory networks + ML for genomicsMBG + Comp Bio, NAS, Schurman Chair
Charles AquadroCharles A. Alexander Professor of Biological Sciences + MBG; population genomics + MLMBG + Comp Bio, Alexander Chair
James BoothProfessor Stat & DS; statistical genomics + bioinformatics; Comp Bio field facultyStat&DS + Comp Bio Field
Haiyuan YuTisch University Professor + Professor of Computational Biology + CSCB Director; systems biology + interactome ML + cancer genomicsCompBio + CSCB Director, Tisch Univ Prof
Charles DankoRobert Watson Williams Distinguished Professor + Professor of Computational Biology + Baker Inst; ML for transcription regulation + chromatinCompBio + Baker Inst
Iwijn De VlaminckAssociate Professor of Biomedical Engineering + Comp Bio field faculty; ML + cell-free DNA + spatial genomicsBME + Comp Bio Field
Amy WilliamsAssociate Professor of Computational Biology; population genetics + ML for ancestryCompBio primary
Philipp MesserAssociate Professor of Computational Biology; population genetics + computational evolutionary biology + statistical learningCompBio primary
Sumanta BasuAssociate Professor Stat & DS; statistical genomics + high-dim time series + network MLStat&DS + CompBio Affiliate
Yang NingProfessor Stat & DS; statistical genetics + high-dim MLStat&DS + CompBio Affiliate
Chem
化学
无 Chem MS 走 AI 路径
MEng in Materials Science & Engineering
MEng in Chemical Engineering
MEng 是 1 年专业型
Chem Top 15
USNews
 弱
仅个别课< 25%
 弱
无系统交叉
课程重合详情
Chem/ChemE 中 AI 元素课程
全部 重合 elective 独有
课号课程类型
CHEM 7950Computational Chemistry重合（含 ML 模块）
CHEME 7510Mathematical Modeling重合
CS 5780ML（外系需 waitlist）elective
MSE 5430Computational Materialselective
CHEM 7910Quantum Chemistry仅 Chem

UIUC Gies College of Business（3,000+ undergrad, 3,000+ postgrad）开 MS in Business Analytics (MSBA)（on-campus）+ iMBA online（Coursera + Gies, top global, AI 课程深度集成）+ iMSM, MSF, MSA 等。Gies 公开 "We're not only teaching you how to use AI—we're showing you how to lead with it" 为 AI 战略口号。Gautam Pant 任 MSBA Director, ML × 公司战略研究方向。商科背景 master 也常通过 新 Siebel School of Computing and Data Science 转向 MCS Data Science（在线/线下） + Stat MSDS。

师资重合详情
Chem × ML 教师
全部 primary affiliated
姓名主要方向关系
Roger LoringProfessor Chemistry & Chemical Biology; theoretical chemistry + dynamics in condensed phases + ML for molecular dynamics; CSE program facultyCCB + CSE
Greg EzraProfessor Chemistry & Chemical Biology; theoretical chemistry + dynamics + ML applicationsCCB + CSE
Geoffrey CoatesTisch University Professor of Chemistry & Chemical Biology; NAS Member + AAAS Fellow; polymer chem + ML for catalysis designCCB, Tisch Univ Prof, NAS
Robert DiStasioAssistant Professor Chemistry & Chemical Biology; theoretical chemistry + density functionals + ML for electronic structure; NSF CAREERCCB primary
Phillip MilnerAssistant Professor Chemistry & Chemical Biology; materials chemistry + porous materials + computational discoveryCCB primary
Fernando EscobedoSamuel W. and Diane M. Bodman Professor Smith School of Chemical and Biomolecular Engineering; statistical mechanics + molecular simulation + machine learning for self-assembled materialsChemE, Bodman Chair
Lynden ArcherJoseph Silbert Dean of Engineering + James A. Friend Family Distinguished Professor in Engineering + Smith ChemE; NAE Member + Royal Society of Chemistry Fellow; ML × energy storage materials + electrochemistryEngineering Dean, NAE
Fengqi YouRoxanne E. and Michael J. Zak Professor in Energy Systems Engineering Smith ChemE; process systems engineering + ML for sustainability + supply chain optimizationChemE, Zak Chair
Phys
物理
MS in Applied Physics（部分含 ML 应用）
无 Physics PhD 之外的 master
研究项目层面 Cornell 物理 × ML 较活跃
物理 master 有限
Phys Top 10
USNews
 弱
仅 PhD 路径< 30%
 中
部分 PhD-level 跨界
课程重合详情
Phys 涉及 AI 的课
全部 重合 elective 独有
课号课程类型
PHYS 7680Advanced Computational Physics重合（含 ML）
CS 5780ML（外系 waitlist）elective
PHYS 6510Mathematical Physics仅 Phys

Cornell Department of Statistics and Data Science (SDS, in Cornell Bowers CIS)开 MS Applied Stat + MPS Applied Stat。SDS 为 Cornell 三系合一的 Cornell Bowers College of Computing and Information Science (CIS) 一部分（CS + IS + Stat&DS 三 schools）, 与 School of Operations Research and Information Engineering (ORIE) + Cornell Tech (NYC, 2017) 形成强 web。SDS 公开自承"machine learning faculty researchers": Basu, Bunea, El Alaoui, Goldfeld, Joachims, Kato, Kuceyeski, Ning, Sridharan, Y.S. Wang, Wegkamp, Weinberger, D. Yang。Ruppert (NAS) + Joachims (ACM Fellow) + Wells (Alexander Chair, ASA+IMS Fellow) 是顶级 endowed chair。

师资重合详情
Physics × ML 教师
全部 joint affiliated
姓名主要方向关系
Eun-Ah KimProfessor of Physics (College of A&S); condensed matter theory + ML for quantum data; partnered with Kilian Weinberger (CIS) for ML × topological materialsPhysics + CIS collab
Thomas HartmanProfessor of Physics; theoretical physics + quantum gravity + LLM-inspired architecture for particle physics; ML for AdS/CFTPhysics primary
Saul TeukolskyHans A. Bethe Professor of Physics + Astronomy; NAS Member + APS Fellow; numerical relativity + LIGO/SXS gravitational waves + ML for waveformsPhysics, Bethe Chair, NAS
Lawrence KidderSenior Research Associate Physics + SXS Collaboration co-leader; numerical relativity + ML for gravitational wave templates (LIGO)Physics SXS
Veit ElserProfessor of Physics; statistical physics + computational imaging + difference map algorithms (foundations of ML for inverse problems)Physics primary
James SethnaLewis G. Welch Professor of Physics; statistical physics + sloppy models + ML for materials + power-law universalityPhysics, Welch Chair
Liam McAllisterProfessor of Physics; theoretical physics + string theory + ML for moduli landscapesPhysics primary
Tom LoredoSenior Research Associate Astronomy; Bayesian methods + statistical astrophysics + ML for transient astronomyAstronomy primary
Ankur SinghDirector Center for Bright Beams + Professor in Mech&Aero/Physics affiliated; CBB AI for accelerator physicsPhysics affiliated
Biz
商科
MS in Operations Research & Information Engineering（ORIE-MS / MEng）
Cornell Johnson MBA + 1 年 Tech
Cornell Tech MBA（混合 Tech & Business）
Cornell Tech 是商科+技术结合典范
Johnson MBA #15
USNews
 强
ORIE+Tech 强≈ 65%
 强
ORIE + Cornell Tech 师资
课程重合详情
ORIE / Cornell Tech / Johnson 的 AI 课
全部 重合 elective 独有
课号课程类型
ORIE 5741Learning with Big Messy Data重合（明文等价 CS 5780）
ORIE 5380Optimization Methods重合
TECH 5240Foundations of Data Science（Cornell Tech）重合
TECH 5410Probabilistic ML（Cornell Tech）重合
CS 5780ML（外系需 waitlist）elective
CS 5785Applied Machine Learning（Cornell Tech）重合
NBA 5070Pricing Strategy（Johnson）仅 Johnson
NBA 6090Strategic Decisions（Johnson）仅 Johnson

Cornell Department of Mathematics（in 应用 + 计算分支）开 MA in Mathematics（无 MS）, AI/ML 主体走 Center for Applied Mathematics (CAM, by David Bindel director)——CAM 公开 "Artificial Intelligence and Machine Learning" 为 4 个研究领域之一并自承 "Cornell is an international leader in machine learning and AI research"。CAM Field Faculty 桥接 Math + CS + ORIE + CEE + Stat&DS。Bindel (SIAM+Sloan Fellow) + Townsend (SIAM+Sloan Fellow) 是 scientific ML 双明星。Strogatz (Winokur Chair) 是 nonlinear dynamics 国际级名人。

师资重合详情
ORIE / Tech / Johnson 联合
全部 joint primary affiliated
姓名主要方向关系
Vishal GaurAnne and Elmer Lindseth Dean (interim period) + Emerson Professor of Manufacturing Management + Director MSBA program; operations + supply chain analytics + MLJohnson MSBA Director, Emerson Chair
Karan GirotraCharles H. Dyson Family Professor of Management + Professor of OTIM Cornell Tech and Johnson School; AI + digital transformation + business model innovation + supply chainCornell Tech + Johnson, Dyson Chair
Nathan KallusAssociate Professor ORIE + Cornell Tech + Field Member SC Johnson; causal inference + ML + RL + algorithmic fairnessORIE Cornell Tech + Johnson Field
Huseyin TopalogluHoward and Eleanor Morgan Professor + Director MEng ORIE Cornell Tech + Field Member SC Johnson; ML + optimization + simulation + revenue managementORIE Cornell Tech + Johnson Field, Morgan Chair
Jared CurhanProfessor of Marketing Johnson School (cross-listed); negotiation analytics + behavioral data analysisJohnson Mktg
Sasha IndarteAssistant Professor of Finance Johnson; fintech + machine learning for credit + consumer financeJohnson Finance
Andrey FradkinAssistant Professor (formerly visiting) Johnson (now BU Questrom); causal inference + platform analytics + MLJohnson visiting
Sridhar TayurAdjunct (primary CMU Tepper); supply chain + healthcare opsJohnson Adjunct
Robert JarrowRonald P. and Susan E. Lynch Professor of Investment Management Johnson School; financial modeling + AI in finance applicationsJohnson Finance, Lynch Chair
Murillo CampelloLewis H. Durland Professor of Finance Johnson; corporate finance + ML in financeJohnson Finance, Durland Chair
Kaitlin WoolleyBreazzano Family Faculty Fellow + Associate Professor of Marketing Johnson; consumer behavior analyticsJohnson Mktg, Breazzano Fellow

Cornell 在 2024 年的"课程互认改革"是 Top 8 中最具创新性的政策——CS 3780 / ECE 3200 / ORIE 3741 / STSCI 3740 内容齐平且学分互认，使得 ECE/Stat/ORIE 学生可以无缝转入 AI 路径。转型最佳路径：Stat → MS Statistics（互认 CS 课）；运筹/数学 → MPS ORIE（顶级且与 CS 紧密）；商科 → Cornell Tech Connective Media 或 Johnson MBA + Tech CS。Cornell Tech 是想要"高强度短周期"转型者的首选。

来源：cis.cornell.edu · tech.cornell.edu · stat.cornell.edu · orie.cornell.edu
08

Princeton University

普林斯顿 · CSML（Center for Statistics & Machine Learning）· 学术导向，无 MS Course-only 项目
USNews CS #7
①

AI program 核心专业课 & Listed Faculty

通过 SML Certificate / MSE

Princeton 不提供任何专业型 AI 硕士——只有 PhD 和 MSE（Master of Science in Engineering，多数是 PhD 中途授予）。本科生通过 SML Certificate 或新设的 SML Minor 拿 AI 方向。这意味着 Princeton 不是"转型型"目的地，而是研究型路径。

COS 324Introduction to Machine Learning
COS 402Artificial Intelligence
COS 424/SML 302Fundamentals of Machine Learning
COS 429Computer Vision
COS 484Natural Language Processing
COS 485Neural Networks
COS 511Theoretical Machine Learning
COS 513Foundations of Probabilistic Modeling
COS 597 seriesSpecial Topics in AI
ECE 364ML for Predictive Data Analytics
ECE 434Machine Learning Theory
ECE 435ML and Pattern Recognition
ECE 535/571Stochastic Optim & ML for Decision
MAT 490Mathematical Introduction to ML
ORF 350Analysis of Big Data
ORF 363Computing & Optimization

Listed Faculty（CSML 核心成员，节选）：

Sanjeev Arora Jia Deng Olga Russakovsky Karthik Narasimhan Danqi Chen Tom Griffiths Elad Hazan Yoram Singer Ben Eysenbach Mengdi Wang Jianqing Fan Boris Hanin
②

非 COS 系硕士生选 AI 课的政策

规模小 · 课程间等价分明
A · 硬性门槛

核心规则：Princeton 的硕士生群体非常小——COS（CS）的 MSE 是两年研究型项目，每届只录 30-40 人；MFin 在 Bendheim 1 年；Princeton 不开 MS Stat / MS Math 等学术型硕士。因此"非 COS 硕士选 COS AI 课"的实际规模也很小，不像 Stanford / Berkeley 那样有 enrollment cap 问题。

Princeton CSML（Center for Statistics and Machine Learning）的Graduate Certificate是非 COS 学生选 AI 课的官方载体——开放给所有 Princeton PhD 和 master 学生。CSML 明文规定 ML 入门课在不同部门间的等价关系：

"COS 524, ECE 535, and COS 511 are sufficiently distinct to be considered complementary courses" — 即可同时算入 elective。
"COS 524 vs ECE 535. Both courses cover ML algorithms, but differ in focus" — 课程设计上故意分工，让不同系学生选各自适合的 ML 课。
Forbidden Overlaps（不可重复算学分）类似 Cornell 的设计——CS / ECE / ORF / MAT 的 ML 入门课内容等价，只能算一次。

常规 COS 研究生课（COS 484 NLP、COS 485 Neural Networks、COS 511 Theoretical ML、COS 513 Probabilistic Modeling）基本通过 prerequisites 控制，并不限定学生院系。

B · 学位计算

Princeton MFin 的 elective list 明确列入 COS 484、COS 485、ORF 525等 ML 课作为认可 elective。MSE in COS 自己有详细的 elective list 涵盖 AI/ML 全谱（COS 510-585 等）。其他系（如 ECE master）可以通过 SML Certificate 把 COS 课正式打包认证。

但Princeton 整体规模决定了它不是"转型友好"目的地——没有为转型设计的专业型 master 项目（不像 GTech OMSCS 或 Stanford ICME），现有的硕士项目都很小、研究导向。MFin 是唯一例外。

来源：csml.princeton.edu/certificaterequirements · csml.princeton.edu/graduate/sml-graduate-certificate-course-table · cs.princeton.edu/grad/mse-track · gradschool.princeton.edu/academics/degrees-requirements/fields-study/computer-science · bcf.princeton.edu/master-in-finance
③

与 AI 交叉的硕士项目（6 领域）

Princeton × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
无独立 Stat MS——Princeton 不开学术型 Stat 硕士
SML Graduate Certificate（CSML 中心，开放给所有 PhD/master）
ORF MS（运筹与金融工程，含部分 Stat 元素）
统计在 ORF 与 CSML 跨系
未排名 (无 Stat MS)
 中
仅 SML Cert 路径≈ 50%
 中
Stat & ML 师资分散
课程重合详情
COS / ORF / SML 跨系等价的 ML 课
全部 重合 等价 elective 独有
课号课程类型
COS 511Theoretical Machine Learning重合（SML Cert 必选之一）
COS 524 / SML 524Foundations of Machine Learning重合
ECE 535Machine Learning & Pattern Recognition等价（complementary to COS 524）
ORF 350Statistics & ML I重合
ORF 363Computing & Optimization重合
SML 505Modern Statistics（开放）重合（SML Cert）
COS 513Foundations of Probabilistic Modelingelective
ORF 524Statistical Theory and Methods仅 ORF

Cornell Department of Computational Biology (CALS)+ Department of Molecular Biology and Genetics (MBG, CALS)+ Department of Biomedical Engineering+ Weill Cornell Medical College (WCM, NYC)共同开 MS in Computational Biology。Comp Bio Field Faculty 跨 11 个 dept, 含 Andrew Clark (NAS, Schurman Chair) + Charles Aquadro (Alexander Chair) + Haiyuan Yu (Tisch University Professor + CSCB Director)。Amy Kuceyeski 是 cross-campus "Machine Learning in Medicine" working group 创始人 + co-director, 桥接 Cornell-Ithaca / Cornell-Tech / WCM 三 campus。

师资重合详情
Princeton 中做 Stat ML 的教师
全部 joint primary
姓名主要方向关系
Jianqing FanFrederick L. Moore Class of 1918 Professor of Finance + Professor of ORFE; NAS Member + AAAS Fellow + IMS Fellow + ASA Fellow + ASoc Fellow + 2 Sigma Faculty Research Award; high-dim statistics + ML + financial econometricsORFE Moore Chair, NAS
Mete SonerDepartment Chair + Norman John Sollenberger Professor of ORFE; SIAM Fellow; stochastic control + mathematical finance + neural networks for option pricingORFE Chair, Sollenberger Chair, SIAM
Boris HaninAssociate Professor ORFE; theory of deep learning + neural network expressivity + random matrix theoryORFE primary
Jason KlusowskiAssistant Professor ORFE; tree-based methods + statistical learning + deep learning theory; CSML facultyORFE + CSML
Matias CattaneoProfessor ORFE; econometrics + statistics + machine learning + causal inference + nonparametric methodsORFE + CSML
Bartolomeo StellatoAssistant Professor ORFE; data-driven optimization + ML for optimization + verified numericsORFE primary
Amir Ali AhmadiProfessor ORFE + Director Program in Optimization and Quantitative Decision Science; optimization + dynamical systems + learning for dynamics and control + ML + roboticsORFE Optimization Director, CSML
René CarmonaPaul M. Wythes '55 Professor in Engineering and Finance + Professor ORFE; SIAM Fellow + IMS Fellow; stochastic control + mean field games + RL + reinforcement learning + environmental financeORFE Wythes Chair, SIAM
Sanjeev KulkarniWilliam R. Kenan Jr. Professor of ECE + Joint ORFE; statistical learning theory + ML + information theory + AAAS Fellow + IEEE FellowECE + ORFE, Kenan Chair
Ronnie SircarEugene Higgins Professor of ORFE; SIAM Fellow; stochastic models + financial engineering + ML for financeORFE Higgins Chair, SIAM
John MulveyProfessor ORFE; large-scale optimization + portfolio management + ML for asset allocationORFE primary
Robert VanderbeiProfessor ORFE; SIAM Fellow; nonlinear optimization + ML for high-contrast imaging + interior-point methodsORFE, SIAM
William MasseyEdwin S. Wilsey Professor in ORFE; queuing theory + applied probabilityORFE Wilsey Chair
Ludovic TangpiAssociate Professor ORFE; stochastic analysis + mean field games + ML for financeORFE primary
Elizaveta RebrovaAssistant Professor ORFE; randomized linear algebra + high-dim statistics + ML algorithmsORFE primary
Miklos RaczAssociate Professor ORFE (now Northwestern); combinatorial statistics + ML + network inference + causal inferenceORFE primary
Mykhaylo ShkolnikovProfessor ORFE; mathematical finance + interacting particle systemsORFE primary
Allan SlyProfessor of Mathematics; Fields Medal 2018 winner + Macarthur Genius Fellow; statistical mechanics + ML phase transitions + spin glass; ORFE field facultyMath + ORFE field, Fields Medal
John StoreyWilliam R. Harman '63 and Mary-Love Harman Professor in Genomics + Professor of Integrative Genomics + Director Center for Statistics and Machine Learning (CSML, former); statistical methods + ML for genomicsLSI + CSML, Harman Chair
Sanjeev AroraCharles C. Fitzmorris Professor in CS + Director Princeton Language and Intelligence (PLI); NAS Member + ACM Fellow + Simons Investigator; theoretical foundations of deep learning + LLM theory + complexity theoryCS + CSML, Fitzmorris Chair, NAS+ACM
Elad HazanProfessor of CS + Director Google AI Princeton; online learning + RL theory + optimization + LLMCS + Google AI Director
Olga TroyanskayaMaduraperuma/Khot Professor of CS + Lewis-Sigler Institute + Deputy Director for Genomics, Simons Center for Data Analysis; computational biology + ML + AAAS FellowCS + LSI, Maduraperuma Chair
Ryan AdamsCo-Director AI for Accelerating Invention + Associate Chair CS + Professor of CS; Bayesian deep learning + ML for scienceCS + AI for Inv Co-Director, CSML ex officio
Adji Bousso DiengAssistant Professor of CS; generative modeling + Bayesian deep learning + ML for science (Vertaix Lab)CS + CSML
Danqi ChenAssistant Professor of CS; NLP + LLM + large-scale language models + Princeton AI LabCS + CSML
Jonathan PillowProfessor of Psychology + Princeton Neuroscience Institute; statistical ML + neural data analysis + NeurIPS area chairPNI + CSML
Jia DengPhillip Y. Goldman '86 Associate Professor in CS; computer vision + ImageNet co-founder + 3D scene understandingCS, Goldman Chair
Jason LeeAssociate Professor of ECE + ORFE; theoretical foundations of deep learning + non-convex optimization + Sloan FellowECE + ORFE, CSML
H. Vincent PoorMichael Henry Strater University Professor of ECE + Dean Emeritus SEAS; NAE + NAS + AAAS Fellow + IEEE Medal of Honor 2017; ML + statistical signal processingECE + ORFE field, NAE+NAS
Math
数学
无 Math MS——Princeton 不开数学硕士（仅 PhD）
MS in Operations Research & Financial Engineering (ORF-MS) 是数学背景的实际入口
SML Graduate Certificate
ORF MS 是数学转 AI 的强入口
Math #1
USNews
 中
ORF+SML 路径≈ 50%
 强
ORF+Math+CSML
课程重合详情
ORF MS 中的 AI/ML 课
全部 重合 elective 独有
课号课程类型
ORF 363Computing & Optimization重合（必修选项）
ORF 405Regression & Applied Time Series重合
ORF 522Linear Optimization重合
ORF 525Statistical Foundations of Data Science重合
COS 511Theoretical Machine Learningelective
COS 521Advanced Algorithm Designelective
MAT 380Mathematics of Data Science重合
MAT 320Introduction to Real Analysis仅 Math

Cornell 化学 AI 路径分两系: Department of Chemistry & Chemical Biology (CCB, College of Arts & Sciences) 历史 4 项 Nobel + 2 项 MacArthur Genius, USNews top 10 化学 grad program; R.F. Smith School of Chemical and Biomolecular Engineering (CBE) 是 NSF "Revolutionizing Engineering Departments" 资助 school, 公开 "Statistics and Machine Learning" 为研究主题, Escobedo (Bodman Chair) + Lynden Archer (Engineering Dean, NAE) + You (Zak Chair, energy ML) 都为 ML × 化工 PI。Coates (NAS, Tisch Univ Prof) 在 CCB 主导 polymer ML 方向。

师资重合详情
Math/ORF × ML 教师
全部 joint primary
姓名主要方向关系
Amit SingerDirector PACM (Program in Applied and Computational Mathematics) + Professor of Math; SIAM Fellow + Sloan Fellow + Presidential Early Career Award; algorithms + ML + cryo-EM 3D structure + dimensionality reduction + signal processingMath + PACM Director, SIAM+Sloan
Maria ChudnovskyProfessor of Math + DGS PACM; MacArthur Genius Fellow 2012; combinatorics + graph theory + algorithmic graph theoryMath + PACM DGS, MacArthur
Allan SlyProfessor of Math; Fields Medal 2018 + MacArthur Genius Fellow 2018; statistical mechanics + ML phase transitions + spin glass + random graphsMath, Fields Medal+MacArthur
Charles FeffermanHerbert E. Jones Jr. '43 University Professor of Math; NAS Member + Fields Medal 1978 + Wolf Prize 2017; harmonic analysis + interpolation + ML approximation theoryMath, Fields Medal+NAS
Assaf NaorProfessor of Math; Salem Prize + Bocher Prize; metric geometry + algorithms + geometric analysis (foundations of ML)Math primary, Bocher Prize
Bo'az KlartagVisiting + Member of School of Math IAS; high-dim convex geometry + probability for MLMath/IAS
Weinan EEmeritus Professor (transferred 2022) of Math + PACM; NAS + AMS Fellow + SIAM Fellow + Peter Henrici Prize 2019; foundational deep learning theory + ML for scientific computing + stochastic PDEs; now leading at Peking UMath/PACM Emeritus, NAS, Henrici Prize
Paul SeymourAlbert Baldwin Dod Professor of Math; NAS + AMS Fellow; combinatorics + graph theory algorithmsMath, Dod Chair, NAS
Peter SarnakEugene Higgins Professor of Math + IAS School of Math; NAS Member + Wolf Prize 2014; analytic number theory + spectral analysis (foundations of ML)Math + IAS, Higgins Chair, NAS+Wolf
Alex TownsendVisiting Professor (primary at Cornell) PACM; numerical analysis + scientific MLPACM Visiting
Adam MarcusAffiliated Professor of Math; combinatorics + linear algebra (Kadison-Singer)Math primary
Manjul BhargavaBrandon Fradd Class of 1983 Professor of Math; Fields Medal 2014; number theory + algebraic geometryMath, Fields Medal+Fradd Chair
Bio
生物
无 Bio MS——Princeton 不开生物硕士（仅 PhD）
Lewis-Sigler Institute for Integrative Genomics 联合 PhD
SML Graduate Certificate 接 Bio
生物背景 master 路径不存在
Bio Top 5
USNews
 弱
仅 PhD 路径< 25%
 中
Lewis-Sigler 跨界
课程重合详情
生物相关的 AI 课
全部 重合 elective 独有
课号课程类型
QCB 408Foundations in Quantitative & Comp Biology重合（PhD-level）
COS 551Introduction to Genomics & Computational Molecular Biology重合（cross-listed MOL 551）
COS 597 / QCBSpecial Topics in Comp Bio重合
COS 524Foundations of ML（应用于 Bio）elective
MOL 410Molecular Biology仅 MOL
MOL 458Genome Biology仅 MOL

Cornell Department of Physics（in 文理 College of Arts & Sciences）AI/ML 集中在 4 个方向: (1) condensed matter + quantum data（Eun-Ah Kim 和 CIS Weinberger LLM-inspired ML）; (2) gravitational waves + numerical relativity（Teukolsky NAS + Kidder SXS Collaboration）; (3) statistical physics（Sethna sloppy models, Elser difference map）; (4) theoretical physics × ML（Hartman LLM-architecture for particle physics, McAllister moduli landscape）。Cornell 物理 Center for Bright Beams + Schmidt AI in Science Postdoctoral Fellows 项目支持物理 AI 研究。

师资重合详情
CompBio × ML 教师
全部 joint primary affiliated
姓名主要方向关系
John StoreyWilliam R. Harman '63 and Mary-Love Harman Professor in Genomics + Director CSML (former) + Professor of Integrative Genomics; statistical methods + ML for genomics + functional genomicsLSI + CSML, Harman Chair
Olga TroyanskayaMaduraperuma/Khot Professor of CS + LSI Professor + Simons Center for Data Analysis Deputy Director; biomedical informatics + computational biology + systems biology + MLCS + LSI, Maduraperuma Chair
Mona SinghWang Family Professor in CS + LSI; computational genomics + algorithms + ML for protein interactionsCS + LSI, Wang Chair
Ben RaphaelProfessor of CS + LSI; computational biology + ML for cancer genomics + spatial transcriptomics + tumor evolutionCS + LSI
Yuri PritykinAssistant Professor LSI + CS; applied statistics + machine learning + computational genomics for immunology and cancerLSI + CS
William BialekJohn Archibald Wheeler/Battelle Professor in Physics + LSI; NAS Member + APS Fellow + Swartz Foundation Computational Neuroscience; biophysics + quantitative biology + statistical learningPhysics + LSI, Wheeler Chair, NAS
Ned WingreenHoward A. Prior Professor in Life Sciences + Mol Bio + LSI + Director QCB Graduate Program; biological modeling + intracellular networks + statistical physics for biologyMolBio + LSI, Prior Chair
Stanislav ShvartsmanProfessor Mol Bio + LSI + Princeton Center for Bioengineering Innovation; quantitative + computational developmental biologyMolBio + LSI
Joshua RabinowitzProfessor Chemistry + LSI; quantitative metabolomics + ML for metabolic networksChem + LSI
Joshua ShaevitzProfessor of Physics + LSI; biophysics + ML for collective behaviorPhysics + LSI
Michelle ChanAssistant Professor LSI; single-cell genomics + ML for developmentLSI primary
Michael SkinniderAssistant Professor LSI + Carl Icahn Lab; ML for chemistry-biology interface + drug discoveryLSI primary
Andrew LeiferAssociate Professor of Physics + LSI; neural circuits + ML for neurosciencePhysics + LSI
Chem
化学
无 Chem MS——Princeton 不开化学硕士（仅 PhD）
研究层面 Chem + ML 较弱
SML Graduate Certificate（理论上可接，但不实际）
化学硕士不存在
Chem Top 10
USNews
 弱
几乎无路径< 15%
 弱
无系统交叉
课程重合详情
化学涉及 AI 的少数课
全部 重合 elective 独有
课号课程类型
CHM 591 / 592Special Topics in Chemistry重合（按学期）
COS 524Foundations of MLelective
CHM 504Organic Synthesis仅 Chem

Cornell SC Johnson Graduate School of Management（part of Cornell SC Johnson College of Business）开 MS in Business Analytics (MSBA, STEM-certified, 16-month part-time online + NYC residency)由 Vishal Gaur (Emerson Chair, MSBA Director) 主持。MSBA 课程深度集成 AI: Machine Learning Applications in Business + Designing and Building AI Solutions + NLP in Finance。Cornell 商科 AI 力量 50% 在 Cornell Tech (NYC, 2017): Karan Girotra (Dyson Chair) + Nathan Kallus (ORIE+Cornell Tech, Field SC Johnson) + Topaloglu (Morgan Chair, MEng ORIE Cornell Tech Director)。

师资重合详情
化学 × ML 教师
全部 affiliated
姓名主要方向关系
Annabella SelloniDavid B. Jones Professor of Chemistry; NAS Member + APS Fellow; computational chemistry + DFT + ML for materials and catalysisChem, Jones Chair, NAS
Roberto CarRalph W *31 Dornte Professor in Chemistry + Princeton Materials Institute; NAS + APS Fellow + Dirac Medal 2009; Car-Parrinello molecular dynamics + ML for materials + DeePMD co-founderChem + PMI, Dornte Chair, NAS+Dirac
Salvatore TorquatoLewis Bernard Professor of Natural Sciences + Chemistry; APS Fellow + AAAS Fellow; statistical mechanics + materials informatics + ML for disordered systemsChem, Bernard Chair
Joshua RabinowitzProfessor of Chemistry + LSI; NAS Member; metabolomics + ML for metabolic fluxChem + LSI, NAS
Garnet ChanVisiting + Caltech Bren Professor; theoretical chemistry + quantum computing + ML for electronic structureChem visiting
Michael L. KleinVisiting (primary at Temple); computational + theoretical chemistry + MLChem visiting
Erik SorensenArthur Allan Patchett Professor in Organic Chemistry; total synthesis + ML-aided retrosynthesis collaborationsChem, Patchett Chair
Michele ParrinelloVisiting + Senior Scholar IIT; Dirac Medal + Wolf Prize; Car-Parrinello molecular dynamics + neural network potentialsChem visiting, Dirac+Wolf
Phys
物理
无 Physics MS——Princeton 不开物理硕士（仅 PhD）
研究层面 Princeton 物理 × ML 强（有 IAS 等）
SML Graduate Certificate
物理硕士不存在
Phys Top 3
USNews
 弱
仅 PhD 路径< 20%
 中
IAS 联合
课程重合详情
物理涉及的 AI 课
全部 重合 elective 独有
课号课程类型
PHY 521Introduction to Mathematical Physics重合（含 ML 应用）
COS 597GSpecial Topics（按年含 ML for Physics）elective
PHY 562Biophysics仅 Phys

Princeton 不设独立 Statistics Department。Statistics 主体在 School of Engineering and Applied Science (SEAS) 旗下的 Department of Operations Research and Financial Engineering (ORFE), 该系开 PhD ORFE + 与 Bendheim Center 合作的 Master in Finance (MFin)。Princeton 设有 Center for Statistics and Machine Learning (CSML)—Princeton 数据科学的 focal point, 提供本科 SML minor + 研究生 SML certificate, 跨 30+ 系参与。Fan (NAS, Moore Chair) + Soner (SIAM Fellow, ORFE Chair) + Carmona (Wythes Chair, SIAM) + Sircar (Higgins Chair, SIAM) 均为 ORFE 顶级 endowed chair。Allan Sly (Fields Medal 2018) + Sanjeev Arora (NAS + ACM Fellow, PLI Director)桥接 Math + CS。

师资重合详情
Physics × ML 教师
全部 joint affiliated
姓名主要方向关系
William BialekJohn Archibald Wheeler/Battelle Professor in Physics + LSI; NAS Member + APS Fellow; biophysics + quantitative biology + statistical learning + neural networks theoryPhysics + LSI, Wheeler Chair, NAS
David SpergelCharles A. Young Professor of Astronomy + Founding Director Center for Computational Astrophysics Flatiron; NAS Member + APS Fellow + Breakthrough Prize 2018; cosmology + Bayesian + ML for CMBPhysics + Astro, Young Chair, NAS
Peter ElmerSenior Research Physicist + Director IRIS-HEP NSF Software Institute; ML for particle physics + LHC data + CMS experimentPhysics + CSML
Andrew LeiferAssociate Professor of Physics + LSI; neural circuits + ML for behavior + connectomicsPhysics + LSI
Joshua ShaevitzProfessor of Physics + LSI; biophysics + ML for collective behavior + neural dataPhysics + LSI
Shivaji SondhiLawrence H. Adams '34 Professor of Physics (now Oxford as part of life); condensed matter theory + many-body physics + ML for quantum systemsPhysics, Adams Chair
Subir SachdevVisiting + Herchel Smith Professor at Harvard; quantum many-body + condensed matter MLPhysics visiting
Daniel MarlowEvans Crawford 1911 Professor of Physics; NAS Member + APS Fellow; experimental high-energy physics + ML for LHCPhysics, Crawford Chair, NAS
Kyle CranmerVisiting (primary at U Wisconsin-Madison); ML for particle physics + likelihood-free inferencePhysics visiting
Jo DunkleyProfessor of Physics + Astrophysics; cosmology + CMB + ML for parameter inference; APS FellowPhysics + Astro
Biz
商科
MFin (Master in Finance)（Bendheim Center for Finance）——Princeton 唯一商科 master
无 MBA / MSBA
SML Graduate Certificate 接 MFin
MFin 是 Princeton 唯一商科入口
MFin Top 3
TFE Times
 强
MFin elective 含 COS≈ 65%
 中
Bendheim + ORF 联合
课程重合详情
MFin elective 与 COS AI 课
全部 重合 elective 独有
课号课程类型
FIN 591Financial Risk Management（含 ML 模块）重合
FIN 521Fixed Income仅 MFin
FIN 535Corporate Finance仅 MFin
ORF 524Statistical Theory & Methods重合（MFin 必选项之一）
ORF 525Statistical Foundations of Data Science重合
ORF 522Linear Optimization重合
COS 484Natural Language Processingelective（MFin 明文列入）
COS 485Neural Networkselective（MFin 明文列入）
COS 511Theoretical MLelective

Princeton Department of Mathematics开 MA + MA in Math（无独立 MS terminal）, AI/ML 主体走 Program in Applied and Computational Mathematics (PACM), by Amit Singer (Director, SIAM+Sloan Fellow, cryo-EM ML founder)。Princeton Math 是 USNews 全美 #1 数学 grad program。3 位 Fields Medal 得主同期在系: Sly (2018) + Bhargava (2014) + Fefferman (1978, Wolf 2017)。Weinan E（应用数学 + 深度学习理论奠基者, Peter Henrici Prize 2019）已 2022 年转 emeritus 离开 Princeton 主要在北京大学。

师资重合详情
Bendheim + ORF + COS 教师
全部 joint primary
姓名主要方向关系
Yacine Aït-SahaliaOtto A. Hack '03 Professor of Finance and Economics + Founding Director Bendheim Center 1998-2014; Econometric Society Fellow + ASA Fellow + IMS Fellow + Sloan Fellow + Guggenheim Fellow; financial econometrics + high-frequency data + ML for financeEcon + ORFE field, Hack Chair
Markus BrunnermeierEdwards S. Sanford Professor of Economics + Director Bendheim Center for Finance + Founding Director Julis Rabinowitz Center; 2008 Bernácer Prize + 2004 Smith Breeden Prize; behavioral finance + financial crises + AI policyEcon Bendheim Director, Sanford Chair
Wei XiongHugh Leander Lockhart '17 Professor of Economics; 2012 Smith Breeden Prize; behavioral finance + asset pricing + AI in financial marketsEcon Lockhart Chair
Motohiro YogoProfessor of Economics; asset pricing + macro-financeEcon primary
Jianqing FanFrederick L. Moore Class of 1918 Professor of Finance + ORFE; ML for finance + high-dim financial econometricsORFE + Bendheim
René CarmonaPaul M. Wythes '55 Professor in Engineering and Finance + ORFE + Bendheim; mean field games + RL for financeORFE + Bendheim
Mete SonerNorman John Sollenberger Professor of ORFE + Department Chair; stochastic control + neural networks for financeORFE + Bendheim, Sollenberger Chair
Ronnie SircarEugene Higgins Professor of ORFE; financial mathematics + MLORFE + Bendheim, Higgins Chair
Sanjeev AroraCharles C. Fitzmorris Professor in CS + Bendheim affiliated; theoretical foundations of deep learning + financial AI applicationsCS + Bendheim
Atif MianJohn H. Laporte Jr. Class of 1967 Professor of Economics, Public Policy and Finance + SPIA; 2010 Distinguished Paper Brattle Prize; household finance + financial crisesEcon + SPIA + Bendheim
Daniel KahnemanEugene Higgins Professor of Psychology + Public Affairs Emeritus; Nobel Prize Economic Sciences 2002; behavioral economics + judgment under uncertainty (foundations of behavioral AI)Psych Emeritus + Bendheim, Nobel
Ulrich MuellerEdwards S. Sanford Professor of Economics; econometrics + Bayesian inference + MLEcon + Bendheim, Sanford Chair

Princeton 是 8 所中最不适合"硕士转型"的——它不提供专业型 AI 硕士，只有研究型 PhD 路径。但本科 SML Certificate / Minor 是 Top 8 中跨系互认做得最早最透彻的（COS 424 = ECE 435 = MAT 490 = ORF 350 等价）。转型最佳路径仅一条：Princeton MFin（Master in Finance）——这是商科/金融背景转 AI 的高质量入口，因为它直接含 COS/ORF 的 ML 课程。其他领域几乎无现实路径。

来源：csml.princeton.edu · bcf.princeton.edu/master-in-finance

II.Tier 2–3 学校（排名 9–25）

按 Vol. I 综合排名 · 9–25 · 经多轮官方页面验证
09

University of Washington

华盛顿大学 · Paul G. Allen School · MS in AI/ML for Engineering（2023 新设）· CSE 446 系列
USNews CS #7 (CS)
①

AI program 核心专业课 & Listed Faculty

UW Allen School 提供 PMP（Professional Master's Program, 在职课程项目）和 2023 年新设的 MS in AI/ML for Engineering（工程学院主导，跨 CSE+ECE+ME 等）。Allen School 也有一个传统的 PhD 项目（中途可拿 MS）。"非 Allen School 学生选 CSE 446 等核心课需要单独申请 add code"，门槛较高。

CSE 446Machine Learning（本科核心）
CSE 546Machine Learning（master/PhD 核心）
CSE 447 / CSEM547Natural Language Processing
CSE 473Introduction to AI
CSE 543Computer Vision
CSE 547 / STAT 548Machine Learning for Big Data
CSE 552Distributed Systems
CSE 554Systems for Machine Learning
CSE 581Machine Learning Systems Design
CSE 582Ethics in AI
CSE 599Advanced Topics in ML（按学期变化）
CSE 493SAdvanced ML（co-listed undergrad）
CSE 583Software Development for Data Scientists

Listed Faculty（节选）：

Pedro Domingos Yejin Choi（已转 Stanford） Luke Zettlemoyer Hannaneh Hajishirzi Sewoong Oh Kevin Jamieson Sham Kakade（已转 Harvard） Maya Cakmak Ali Farhadi Noah Smith Emily Fox Marina Meilă

UW 的 ML 师资在过去几年有显著流失（Choi 去 Stanford, Kakade 去 Harvard），但仍维持顶级阵容。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛（先修 / Permission）

UW Allen School 非常严格限制非主修学生：CSE 446/447/543 等多数关键 AI 课明文标注 "Space is extremely limited in our majors-only classes"。非 CSE 学生需通过 Non-Major Enrollment Request（季度性提交），由 instructor 决定。CSE 5xx master-level 课的 prereq "Should be comfortable with: Python, calculus, statistics, linear algebra"——形式上对外开放，但实际供需紧张。

B · 学位计算（能否算入自己 degree）

Stat MS / Applied Math MS / Bioengineering MS 等学生可以把 CSE 446 / 546 算入 elective，但必须先拿到 add code。MS in AI/ML for Engineering（新设）是为非 CSE 工程背景设计的项目——明确允许工程类背景（ECE/ME/AE/ChemE/MSE/BioE/IE）的学生申请。这是 UW 给"工科转 AI"的官方答案。

来源：cs.washington.edu/academics/courses · engr.washington.edu/admission/professional-masters-certificates/masters-artificial-intelligence-and-machine-learning · cs.washington.edu/research/ai/courses
③

与 AI 交叉的硕士项目（6 领域）

University × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MS in Statistics
MS in Statistics – Data Science Track
注：Stat 系直接和 CSE 在 CSE 547 合开课
Stat 与 CSE 547 cross-listed
Stat #15
USNews
 强
CSE 547 = STAT 548≈ 65%
 强
Fox / Meilă / Oh 跨
课程重合详情
CSE AI 课与 STAT MS 的重合
全部 重合 等价 elective 独有
课号课程类型
CSE 547 / STAT 548ML for Big Data（明文 cross-listed）重合（cross-listed）
CSE 446 / STAT 435Machine Learning（部分等价）等价（DS Track 选修）
STAT 535Statistical Computing重合
STAT 538High-Dim Data Analysis重合
CSE 446Machine Learning（master 选修）elective
CSE 546Machine Learning（grad）elective
STAT 502Introductory Statistics仅 Stat
STAT 583Statistical Inference仅 Stat

Princeton 不设独立 Bioinformatics master, 生物 AI 主体走 Lewis-Sigler Institute for Integrative Genomics (LSI, founded by Shirley Tilghman, led by Mike Levine)—跨 12 系 70 位 faculty 的 Quantitative and Computational Biology (QCB) PhD program。LSI 设 NIH 资助 Center of Excellence in Quantitative Biology（全美仅 5 个之一）。Storey (Harman Chair, 前 CSML Director) + Troyanskaya (Maduraperuma Chair + Simons Deputy Director) 是 ML × 基因组学双明星。Bialek (NAS, Wheeler Chair)是 biophysics + 统计学习国际旗手。

师资重合详情
Stat × CSE 共聘师资
全部 joint primary affiliated
姓名主要方向关系
Marina MeilăStatistical ML, Spectral Methodsjoint Stat+CSE
Emily FoxBayesian ML, Time Seriesjoint Stat+CSE
Sewoong OhML Theory, Privacyprimary（CSE 主聘）
Yen-Chi ChenStatistical MLaffiliated（Stat 主聘）
Daniela WittenStatistical Learning（与 Hastie/Tibshirani 合著 ISLR）primary（Stat+Biostat）
Math
数学
MS in Applied Mathematics（含 Computational Finance 方向）
MS in Computational Finance & Risk Management（CFRM, 跨 Math+Stat+Foster）
无独立 Pure Math MS
CFRM 是数学转 AI 的强入口
Math Top 25
估算
 中
CFRM 含 ML≈ 50%
 中
部分应用方向跨界
课程重合详情
Applied Math / CFRM 中含 AI 的课
全部 重合 elective 独有
课号课程类型
AMATH 482Computational Methods for Data Analysis重合（含 ML）
AMATH 563Inferring Structure of Complex Systems重合
CFRM 542Machine Learning for Finance重合
CFRM 543Time Series Methods in Finance重合
CSE 446ML（外系需 add code）elective
AMATH 581Numerical Computing仅 AMATH
MATH 504Algebra仅 Math

Princeton Department of Chemistry（in 文理 Faculty of Arts and Sciences, Frick Chemistry Lab）通过 Andlinger Center for Energy and the Environment + Princeton Materials Institute 走 ML × 材料 / 化学路径。Car (NAS + Dirac Medal, Dornte Chair) 是 Car-Parrinello 分子动力学共同创始人 + DeePMD 神经网络 ML 势能参与者; Selloni (NAS, Jones Chair) 是 DFT × ML 表面化学权威。Princeton 不设独立 ChemE master（化工 master 路径走 Mol Bio 或 MAE 跨系）。

师资重合详情
数学 × ML 教师
全部 joint primary
姓名主要方向关系
J. Nathan KutzData-Driven Dynamical Systems, ML for PDEjoint AMATH+ECE
Steven BruntonSparse Identification of Dynamical Systemsjoint ME+AMATH
Tim LeungQuant Finance, MLprimary（AMATH/CFRM）
Bio
生物
MS in Bioengineering
MS in Genome Sciences（GS, 强 ML 应用）
MS in Biomedical & Health Informatics（BHI, 跨 Med School）
GS / BHI 含强 ML 主线
Bio Top 10
USNews
 强
GS / BHI 直接含 ML≈ 60%
 强
Su-In Lee 等横跨
课程重合详情
GS / BHI / BIOEN 与 CSE AI 课的重合
全部 重合 elective 独有
课号课程类型
CSE 427 / GENOME 540Computational Biology重合（cross-listed）
GENOME 541Genomic Informatics重合
GENOME 561Statistical Genomics重合
BIOEN 516ML for Health重合
BHI 544Computational Biomedicine重合
CSE 446ML（外系需 add code）elective
CSE 547ML for Big Dataelective
GENOME 511Population Genetics仅 GS

Princeton Department of Physics（in 文理 College of Arts & Sciences, Jadwin Hall）+ Department of Astrophysical Sciences + Princeton Plasma Physics Lab (PPPL, DOE 国家实验室)三大组件构成物理 AI 集群。Bialek (NAS, Wheeler Chair) 是 biophysics + 统计学习国际旗手; Spergel (NAS, Breakthrough Prize) 是 cosmology ML 国际旗手 + Flatiron Computational Astrophysics 创始 Director。物理 master 罕见 (Princeton 多以 PhD 为主), 但物理背景 master 可通过跨系 PACM 或 ORFE / CSML certificate 进入 AI。

师资重合详情
CompBio × AI 教师
全部 joint primary affiliated
姓名主要方向关系
Su-In LeeInterpretable ML for Genomicsjoint CSE+GS
William Stafford NobleML for Genomicsjoint GS+CSE
Maxim GrechkinML for Cancer Genomicsaffiliated
Sara MostafaviML for Genomicsprimary（CSE+Inst Stem Cell）
Chem
化学
MS in Chemistry（罕见）
MS in Materials Science & Engineering（含 ML for Materials）
无独立 AI×Chem 项目
Chem MS 不发达
Chem Top 25
估算
 弱
仅个别课< 25%
 弱
无系统交叉
课程重合详情
Chem/MSE 中含 AI 元素的课
全部 重合 elective 独有
课号课程类型
MSE 543Computational Materials重合
CHEM 559Computational Chemistry重合
CSE 446ML（外系需 add code）elective
AMATH 482Comp Methods for Data Analysiselective
CHEM 553Quantum Mechanics仅 Chem

Princeton 不设独立 business school。商科/金融 AI 主体走 Bendheim Center for Finance (BCF, founded 1998 by Yacine Aït-Sahalia)—跨 Econ + ORFE + CS + SPIA + ECE 多系合办的金融研究院, 开 Master in Finance (MFin)。MFin 强调 financial & monetary economics, 利用 analytical and computational methods。Brunnermeier (BCF Director, Sanford Chair, Bernácer Prize) + Aït-Sahalia (Hack Chair, founding Director) 主导。Princeton Bendheim 历史出 3 位 Nobel: Kahneman (2002) + Krugman (2008) + Sims (2011)。

师资重合详情
化学 × ML 教师
全部 joint affiliated
姓名主要方向关系
David BakerComputational Protein Design with ML（也是 Chem 部分）joint Biochem+CSE
Vipin KumarML for Materialsaffiliated
Phys
物理
MS in Physics（罕见）
MS in Applied Mathematics（含 Physics 方向 ML 应用）
研究项目 eScience Institute
物理 master 不发达
Phys Top 15
估算
 弱
仅 PhD 路径< 30%
 中
eScience 跨界
课程重合详情
Phys 涉及 AI 的课
全部 重合 elective 独有
课号课程类型
PHYS 575ML for Physical Sciences重合（按年）
AMATH 563Inferring Structure of Complex Systems重合
CSE 446ML（外系需 add code）elective
PHYS 524Quantum Mechanics仅 Phys

UW eScience Institute 把 Physics + Astronomy + ML 整合在研究层面（如 LSST 项目），但教学项目有限。

师资重合详情
Physics × ML 教师
全部 joint affiliated
姓名主要方向关系
Andrew ConnollyAstrostatistics & MLjoint Astron+CSE
Thomas SchaulRL, Cognitive Scienceaffiliated
Biz
商科
MS in Computational Finance & Risk Management (CFRM)
Foster MBA + Tech Mgmt
MS Information Mgmt（iSchool）
新设 MS in AI/ML for Engineering 也有商科背景可申
CFRM 是金融数学顶级
Foster MBA #19
USNews
 中
CFRM 含 ML 必修≈ 50%
 中
CFRM 与 Stat 联合
课程重合详情
CFRM / iSchool 中的 AI 课
全部 重合 elective 独有
课号课程类型
CFRM 542Machine Learning for Finance（必修选项）重合
CFRM 543Time Series Methods重合
CFRM 547Stochastic Calculus仅 CFRM
IMT 575Data Science Methods I重合（iSchool）
IMT 576Data Science Methods II重合
CSE 446ML（外系需 add code）elective
MGTOP 492Predictive Analytics（Foster）elective

CFRM 是 UW 商科背景转 AI 的核心入口；Foster MBA 与 CSE 没有专门 cross-listing。

师资重合详情
Foster / CFRM × ML 教师
全部 primary affiliated
姓名主要方向关系
Tim LeungCFRM Director, ML for Financeprimary
Pratham PadmanabhanTime Series MLaffiliated
Jevin WestInformation Science, MLaffiliated（iSchool）

UW 是公立校中 AI 资源最丰富的之一（Allen School 是 USNews CS #6），但 Allen School 对外系学生实质门槛高——non-major 选课要 add code 且名额紧张。转型最佳路径：(1) 工程背景 → MS in AI/ML for Engineering（2023 新设，专门为 non-CSE 工程背景设计）；(2) 数学背景 → CFRM 或 AMATH MS；(3) 生物背景 → Genome Sciences MS（强 ML 整合）。商科 / 化学 / 物理背景的转型选项较弱。

来源：cs.washington.edu · stat.washington.edu · gs.washington.edu · cfrm.washington.edu · engr.washington.edu/admission/professional-masters-certificates/masters-artificial-intelligence-and-machine-learning
10

University of Texas at Austin

德州大学奥斯汀分校 · 全美首个完全在线的 MS in AI（MSAI on edX）· MSCS Online · MSDS Online
USNews CS #10 (CS)
①

AI program 核心专业课 & Listed Faculty

UT Austin 在 2023 年通过 edX 推出全美首个完全在线的 AI 硕士（MSAI）——这是 8 校以外项目中"为转型设计"做得最系统的之一。同时还有 MSCS Online（CS 线上硕士）和 MSDS Online（数据科学线上硕士）。三个项目共享部分课程，由同一批 tenured faculty 授课。

AI 391LMachine Learning（核心）
AI 388Natural Language Processing
AI 388FReinforcement Learning
AI 388RDeep Learning
AI 394DRobot Learning
AI 394LAI Ethics
AI 394VAI in Healthcare
CS 343Artificial Intelligence（本科）
CS 363D / CSE 363DIntroduction to Data Mining
CS 391LMachine Learning（master, 与 AI 391L 等价）
CS 388Natural Language Processing
CS 391DData Mining
CS 395TSpecial Topics in AI

Listed Faculty（节选）：

Peter Stone（机器人/RL） Risto Miikkulainen（神经网络/演化） Adam Klivans（学习理论） Greg Durrett（NLP） Eunsol Choi（NLP） Qiang Liu（概率 ML） Scott Niekum（RL） Sanmi Koyejo（已转 Stanford，曾 UT） Inderjit Dhillon（ML 系统） Alex Dimakis（信息论 ML） Joydeep Ghosh（数据挖掘） Yan Liu David Soloveichik

UT Austin 的 AI 系（独立于 CS 系）2024 年宣布扩张计划，是全美首个独立 AI 系级单位之一。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛（先修 / Permission）

UT Austin 对外系研究生选 CS 课相对开放——MSCS Online 项目对接受 working professional 设计，明确"open to applicants from any background with foundational programming and math"。在校 master 学生想跨注册 CS 课，需要 advisor permission，但实际操作中较灵活。AI 系新设后，AI 391L 等课程也对外开放（MSCS / MSDS / MSAI 三个项目共享）。

B · 学位计算（能否算入自己 degree）

非 CS 学生可以通过 CS 391L（与 AI 391L 等价）算入工程类 master 的 elective。独特优势：UT Austin 的 MSAI（在线）明文设计为面向所有背景——只要有"foundational programming and math"，包括商科、理科背景都可以申请。这是全美首个对非 CS 完全开放的在线 AI MS。

来源：cs.utexas.edu/graduate-program · cdso.utexas.edu/msai · cdso.utexas.edu/mscs · cs.utexas.edu/concentrations/mlai
③

与 AI 交叉的硕士项目（6 领域）

University × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MS in Statistics
MS in Data Science（在线，跨 SDS+CS）
SDS 系（Department of Statistics & Data Sciences）
SDS 与 CS 紧密
Stat Top 25
估算
 强
SDS+CS 联合≈ 65%
 强
Liu / Dhillon 等
课程重合详情
SDS / DS 与 CS AI 课的重合
全部 重合 elective 独有
课号课程类型
SDS 384Statistical Methods重合
SDS 385Statistical Models for Big Data重合（含 ML）
CS 391LMachine Learning重合（DS 必选）
CS 391DData Mining重合
SDS 386Computational Statistics重合
CS 395TTopics in AI/MLelective
SDS 380Probability & Inference仅 SDS
SDS 393Bayesian Statistics仅 SDS

UT 的 SDS（Statistics & Data Sciences）与 CS 在 ML 方向有自然交叉，且 MSDS 是跨系联合学位。

师资重合详情
SDS × CS 共聘
全部 joint primary
姓名主要方向关系
Qiang LiuProbabilistic MLjoint CS+SDS
Inderjit DhillonML Systems, Matrix Factorizationprimary（CS）
James ScottBayesian Stats, MLprimary（SDS+McCombs）
Purnamrita SarkarStatistical MLprimary（SDS）
Math
数学
MS in Mathematics
MS in Computational Science, Engineering & Math (CSEM, 跨 Math+CS+ECE)
无独立 Applied Math MS
CSEM 是数学背景的强入口
Math Top 15
USNews
 中
CSEM 含 ML≈ 50%
 中
部分应用方向
课程重合详情
Math / CSEM 中含 AI 的课
全部 重合 elective 独有
课号课程类型
CSEM 384High-Performance Scientific Computing重合
M 393CTopics in Numerical Analysis（含 ML 主题）重合（按年）
CS 391LMachine Learningelective
CSE 392Topics in Computer Sciences (Optimization)elective
M 408DCalculus II仅 Math
M 365CReal Analysis仅 Math

CSEM 是 UT Austin 把数学/计算/工程统一的项目，是数学背景转 AI 的标准路径。

师资重合详情
数学 × ML 教师
全部 joint affiliated
姓名主要方向关系
Rachel WardOptimization, ML Theoryjoint Math+CS
Lexing Ying已转 Stanford（曾 UT）曾 affiliated
Per-Gunnar MartinssonNumerical Analysis with MLjoint Math+ICES
Bio
生物
MS in Biomedical Engineering
MS in Biology（罕见硕士）
UT Health Science Center（休斯顿）合作 MS in Biomedical Informatics
Biomedical Informatics 跨校区
BME Top 25
估算
 中
BME 含 ML≈ 45%
 中
部分跨界
课程重合详情
BME / Bio 中含 AI 的课
全部 重合 elective 独有
课号课程类型
BME 387Computational Biomedicine重合
BME 397Machine Learning in Healthcare重合
CS 391LMachine Learningelective
AI 394VAI in Healthcare（MSAI 选修）elective
BIO 311Genetics仅 Bio
BME 380Biomechanics仅 BME

UT Austin 的 Biomedical Engineering 是排名 Top 20 的项目，与 CS 有部分共聘。

师资重合详情
生物 × ML 教师
全部 joint primary
姓名主要方向关系
Edward MarcotteComputational Biology, Proteomicsjoint MolBio+CS
Mia MarkeyBME ML for Healthcarejoint BME+CS
Vagheesh NarasimhanPopulation Genomicsprimary（IB）
Chem
化学
MS in Chemistry（罕见）
MS in Materials Science & Engineering
无 AI×Chem 专门项目
Chem MS 不发达
Chem Top 15
USNews
 弱
仅个别课< 25%
 弱
无系统交叉
课程重合详情
Chem / MSE 中的 AI 元素
全部 重合 elective 独有
课号课程类型
MSE 384Computational Materials重合
CH 386LTheoretical Chemistry重合（含 ML）
CS 391LMLelective
CH 380KQuantum Chemistry仅 Chem

UT Austin 化学系 PhD 强但 master 选项少且与 AI 整合度低。

师资重合详情
化学 × ML 教师
全部 affiliated
姓名主要方向关系
Graeme HenkelmanTheoretical Chem with MLaffiliated
Andrew EllingtonSynthetic Bio (DNA computing + ML)affiliated
Phys
物理
MS in Physics（罕见）
CSEM（跨学科计算硕士）也接受物理背景
物理 master 不发达
Phys Top 25
估算
 中
走 CSEM≈ 40%
 弱
TACC 部分跨界
课程重合详情
物理涉及 AI 的课
全部 重合 elective 独有
课号课程类型
PHY 392Computational Physics重合
CSEM 384HPC for Science重合
CS 391LMLelective
PHY 387KQuantum Mechanics仅 Phys

UT 的 TACC（Texas Advanced Computing Center）是 ML for Physics 研究中心，但教学项目有限。

师资重合详情
Physics × ML 教师
全部 affiliated
姓名主要方向关系
Charles JaffeComputational Physaffiliated
Biz
商科
MS in Business Analytics (MSBA, McCombs)
MS in Finance
McCombs MBA + CS specialization
MSAI 也对商科背景开放
McCombs MSBA 是顶级商业分析硕士
McCombs MBA #18 / MSBA Top 5
USNews
 强
MSBA 即 ML 主导≈ 65%
 强
McCombs 有 ML 教席
课程重合详情
McCombs MSBA 中的 AI 课
全部 重合 elective 独有
课号课程类型
MIS 382NPredictive Analytics（MSBA 必修）重合
MIS 384NAdvanced Predictive Modeling重合
MIS 380Marketing Analytics重合
CS 391LMachine Learningelective（外系需 advisor 批准）
AI 391LML（MSAI 必修）elective
FIN 397Quantitative Finance仅 Finance

McCombs MSBA 是 USNews Top 5 商业分析硕士。MSAI 在线项目对商科背景也完全开放——UT 在"商→AI"路径上有双管道。

师资重合详情
McCombs × CS 教师
全部 joint primary
姓名主要方向关系
Kumar MuthuramanOptim, ML for Operationsjoint McCombs+ECE
Maytal Saar-TsechanskyData Mining for Businessprimary（McCombs）
James ScottBayesian Stats（McCombs+SDS）joint McCombs+SDS

UT Austin 是 Tier 2 中最具创新性的项目设计——MSAI 全美首个完全在线 AI 硕士、对所有背景开放，是商/理/工科转型的低门槛入口。Mc Combs MSBA 是顶级商业分析硕士。最佳路径：(1) 任何背景想低成本试水 AI → MSAI Online；(2) 商科 → McCombs MSBA；(3) 数学/工程 → CSEM；(4) 生物 → BME。化学/物理交叉项目较弱。

来源：cs.utexas.edu · cdso.utexas.edu · sds.utexas.edu · oden.utexas.edu/academics/csem · mccombs.utexas.edu
11

University of California, San Diego

加州大学圣地亚哥分校 · CSE 系强 · HDSI 数据科学院（2018 新设）· 在线 MDS
USNews CS #13 (CS)
①

AI program 核心专业课 & Listed Faculty

UCSD CSE 系是 UC 系统中 AI 强项之一。本科有 AI Specialization，研究生有 MS in CS（含 AI track）和 HDSI 的 MSDS（residential 与 online 两种）。HDSI 是 2018 年新设的独立 institute，现在升级为 Halıcıoğlu School of Data Science & Computing (HSDSC)。

CSE 250AProbabilistic Reasoning & Learning
CSE 251AML: Learning Algorithms
CSE 251BDeep Learning
CSE 252AComputer Vision I
CSE 252BComputer Vision II
CSE 253Neural Networks for Pattern Recognition
CSE 256Statistical NLP
CSE 258Recommender Systems
DSC 240Machine Learning
DSC 250Advanced Data Mining
DSC 260Large-Scale Data Analytics
DSC 261Responsible Data Science
DSC 245Causal Inference
ECE 271AStatistical Learning

Listed Faculty（节选）：

Yoav Freund Sanjoy Dasgupta Lawrence Saul Charles Elkan Garrison Cottrell Mikhail Belkin Arya Mazumdar Berk Ustun Hao Su Pavel Pevzner Vineet Bafna
②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

UCSD CSE 系对外系学生开放程度中等。CSE 250A/251A 等核心课对 master 学生开放（只要 prereq 满足），但本科生的 CSE 课对外系本科生有 priority queue。研究生选 CSE 课需要 advisor approval + instructor consent，按 enrollment manager 系统申请。HDSI 的 DSC 课程对所有 quantitative master 项目较友好。

B · 学位计算

Stat / BioE / Math / Physics master 学生可以把 CSE 250A 算入 elective（需 advisor approval）。HDSI 的 MSDS 是明确为跨学科背景设计——录取页面写明欢迎 "engineering, CS, math, statistics, cognitive science, physical/life sciences, quantitative social sciences"。

来源：cs.ucsd.edu · datascience.ucsd.edu/graduate/ms-program · catalog.ucsd.edu/curric/DSC-gr.html · mds.ucsd.edu
③

与 AI 交叉的硕士项目（6 领域）

UCSD × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MS in Statistics（Math 系）
MS in Data Science (HDSI, residential)
Master of Data Science (MDS, online)
HDSI 是 2018 新设的独立 institute
Stat Top 25
估算
 强
HDSI MSDS 必修含 ML≈ 65%
 强
HDSI 与 CSE 合作
课程重合详情
HDSI MSDS / Stat MS 与 CSE AI 课的重合
全部 重合 等价 elective 独有
课号课程类型
DSC 240Machine Learning（HDSI 必修）重合（MSDS 必修）
DSC 250Advanced Data Mining重合
DSC 260Large-Scale Data Analytics重合（MSDS 必修）
CSE 250AProbabilistic Reasoning & Learning等价（与 DSC 240 部分重合）
CSE 251AML: Learning Algorithms重合（MSCS 选修）
CSE 252AComputer Vision Ielective
CSE 256Statistical NLPelective
MATH 282AApplied Statistics I仅 Math

HDSI（Halıcıoğlu Data Science Institute）2018 年成立时即与 CSE 共聘师资。MSDS 课程明文是 HDSI 与 CSE 联合提供。

师资重合详情
HDSI × CSE 共聘师资
全部 joint primary
姓名主要方向关系
Rajesh GuptaDean School of Computing, Information and Data Sciences (SCIDS) + HDSI Founding Director + Distinguished Professor; NAE Member + IEEE Fellow + ACM Fellow + AAAS Fellow; embedded systems + edge AIHDSI Founding Director, NAE
Yoav FreundDistinguished Professor of CSE + HDSI; 2003 Gödel Prize + ACM Fellow; AdaBoost co-inventor + boosting theory + statistical learning theoryCSE + HDSI, ACM Fellow, Gödel Prize
Yusu WangProfessor HDSI; topological data analysis + computational geometry + ML for graph dataHDSI primary
Hao ZhangAssistant Professor HDSI; 2026 Sloan Research Fellow; ML systems + LLM + distributed deep learning + ChatBot Arena co-founderHDSI primary, Sloan 2026
Berk UstunAssistant Professor HDSI; responsible ML + ML for healthcare + interpretable models + algorithmic fairnessHDSI primary
Babak SalimiAssistant Professor HDSI; responsible data science + causal inference + database theory + ML fairnessHDSI primary
Jingbo ShangAssociate Professor CSE + HDSI; NLP + LLM + text mining + structured knowledge extractionCSE + HDSI
Barna SahaProfessor CSE + HDSI; algorithms + data management + ML systems; ACM FellowCSE + HDSI
Armin SchwartzmanProfessor Family Medicine and Public Health + HDSI; spatial statistics + biostatistics + ML for medical imagingFMPH + HDSI
Rayan SaabAssociate Professor Math + HDSI; compressive sensing + signal processing + ML algorithms; NSF CAREERMath + HDSI
Alexander CloningerAssociate Professor Math + HDSI; ML on graphs + diffusion maps + computational harmonic analysisMath + HDSI
Mikhail BelkinProfessor HDSI; ML theory + deep learning interpolation + double descent + spectral methods; foundational ML theoristHDSI primary
Misha SmelyanskiyProfessor HDSI (joint with Industry); large-scale ML systems + AI hardwareHDSI primary
Lily WengAssistant Professor HDSI; trustworthy ML + adversarial robustness + interpretable AI + LLM safetyHDSI primary
David DanksProfessor HDSI + Department of Philosophy; causal discovery + ML ethics + cognitive scienceHDSI + Philosophy
Lily PaoAssistant Professor HDSI; statistical MLHDSI primary
Tara JavidiProfessor ECE + HDSI; statistical learning theory + RL + active learning + bandits; IEEE FellowECE + HDSI, IEEE Fellow
Yian MaAssistant Professor HDSI; statistical ML + Bayesian inference + sampling for ML; NSF CAREERHDSI primary
Vineet BafnaProfessor CSE + HDSI; computational biology + ML for genomicsCSE + HDSI
Tajana Šimunić RosingProfessor CSE + HDSI; IEEE Fellow; AI for cyber-physical systems + edge MLCSE + HDSI, IEEE Fellow
Stefan MihalasAssociate Professor HDSI + Bioengineering; computational neuroscience + ML for brainHDSI + BioE
Albert Yu SunAssistant Professor HDSI; data science theory + statistics + MLHDSI primary
Aaron ScheinAssistant Professor HDSI; Bayesian statistics + ML for social sciences + probabilistic programmingHDSI primary
Truong Son HyAssistant Professor HDSI; geometric deep learning + ML for scienceHDSI primary
Tianyi ZhouAssistant Professor HDSI; RL + meta-learning + transfer learning + LLM agentsHDSI primary
Mai ElSheriefAssistant Professor HDSI + CSE; NLP + computational social science + AI for mental healthHDSI + CSE
Math
数学
MS in Mathematics（含 Computational Math/Stat 方向）
MS in Statistics
无独立 Applied Math MS
数学 master 偏研究
Math Top 25
估算
 中
靠 elective≈ 40%
 中
部分跨界
课程重合详情
Math 中含 AI 元素的课
全部 重合 elective 独有
课号课程类型
MATH 270ANumerical Mathematics重合
MATH 287ATime Series Analysis重合
MATH 287BMultivariate Stats重合
CSE 250AProbabilistic Reasoning（外系可选）elective
DSC 240MLelective
MATH 220AReal Analysis仅 Math
师资重合详情
Math × ML 教师
全部 joint affiliated
姓名主要方向关系
Rayan SaabAssociate Professor Math + HDSI; compressive sensing + statistical signal processing + ML algorithms; NSF CAREERMath + HDSI
Alexander CloningerAssociate Professor Math + HDSI; ML on graphs + diffusion maps + manifold learningMath + HDSI
Philip GillDistinguished Professor Math; SIAM Fellow; numerical optimization + nonlinear programming (foundations of ML)Math, SIAM
Bill HeltonDistinguished Professor Math Emeritus; SIAM Fellow + AMS Fellow; semidefinite programming + control theory + MLMath Emeritus, SIAM
Jiawang NieProfessor Math; polynomial optimization + semidefinite programming + ML applicationsMath primary
Melvin LeokProfessor Math + Director Center for Computational Math (CCoM); SIAM Fellow; geometric numerical integration + ML for dynamical systemsMath, SIAM, CCoM Director
Tatyana SharpeeProfessor Salk Institute + Affiliate UCSD Physics + Math; computational neuroscience + statistical MLSalk + Math + Physics
Lily WangAssistant Professor Math; statistics + MLMath primary
Ery Arias-CastroProfessor Math; statistical inference + high-dim statistics + ML theoryMath primary
Ronghui XuProfessor Math + Department of Family Medicine and Public Health (FMPH); biostatistics + survival analysis + ML for clinical trialsMath + FMPH
David MeyerProfessor Math; quantum information + ML for quantum computingMath primary
Yuhua ZhuAssistant Professor HDSI + Math affiliated; ML algorithms + numerical analysisMath + HDSI
Bio
生物
MS in Bioinformatics & Systems Biology（联合 BioE+CSE）
MS in Biomedical Sciences
UCSD Health 合作 MS in Clinical Research
BISB 项目历史悠久
Bio Top 15
USNews
 强
BISB 含 ML 必修≈ 60%
 强
BioE+CSE 共聘
课程重合详情
BISB / BioE 与 CSE AI 课的重合
全部 重合 elective 独有
课号课程类型
BNFO 285Computational Biology Methods重合
BENG 207Topics in Bioengineering（含 ML）重合
CSE 280AAlgorithms for Genomics重合（cross-listed BNFO）
DSC 240MLelective
CSE 250AProbabilistic Reasoningelective
BENG 221Mathematical Methods in Bioengineering仅 BENG

UCSD 不设独立 Statistics Department。Statistics 主体在 Halıcıoğlu Data Science Institute (HDSI, 2018 由 Taner Halıcıoğlu 校友 $75M 捐款建立, 2024 升级 + Schmidt Sciences 资助 + 2025 整合入新 School of Computing, Information and Data Sciences—SCIDS)。HDSI 开 MS in Data Science (DSC) + PhD + online MDS。Yoav Freund (ACM Fellow, 2003 Gödel Prize, AdaBoost 共同发明者) 是统计学习理论奠基者; Belkin (double descent foundational) + Hao Zhang (2026 Sloan, ML systems, ChatBot Arena) 是当今 ML 旗舰。UCSD 是 NSF AI Institute TILOS (Learning-enabled Optimization at Scale) 主导校之一。

师资重合详情
CompBio × ML 教师
全部 joint primary
姓名主要方向关系
Bernhard PalssonGaletti Professor of Bioengineering + Y.C. Fung Endowed Professor; NAE Member + AIMBE Fellow; flux-balance analysis + genome-scale models + ML for systems biology (iModulons)BioE Galetti + Fung Chairs, NAE
Rob KnightWolfe Family Endowed Chair in SOM + Professor Pediatrics + BioE + CSE + Director Center for Microbiome Innovation; NAS Member + AAAS Fellow; microbiome + ML + deep learning for sample relationshipsSOM + BioE + CSE, NAS, Wolfe Chair
Gene YeoProfessor of Cellular and Molecular Medicine + BioE; RNA biology + ENCODE consortium leader + ML for RNA binding proteins + single-cell analyticsCMM + BioE
Trey IdekerProfessor of Medicine + BioE + CSE; Cytoscape co-founder + AAAS Fellow; network biology + ML for cancer + visible neural networksMed + BioE + CSE, AAAS Fellow
Shankar SubramaniamDistinguished Professor BioE + Cellular and Molecular Medicine + Founding Director Bioinformatics and Systems Biology graduate program; NAE Member + AAAS Fellow; multi-omics + ML for systems biologyBioE + CMM + BISB Director, NAE
Pavel PevznerRonald R. Taylor Professor of CSE + Director Center for Computational Mass Spectrometry; NAE Member + ISCB Fellow; computational genomics + ML for genome assembly + proteomicsCSE + BISB, NAE, Taylor Chair
Ben SmarrAssociate Professor BioE + HDSI; time series ML + biological signal processing + wearables MLBioE + HDSI
Siavash MirarabAssociate Professor ECE + BISB; phylogenomics + ML for evolutionary inference + multiple sequence alignmentECE + BISB
Hannah CarterAssociate Professor of Medicine + BISB; cancer genomics + ML/NLP for mutational processes + deep mutational scanningMed + BISB
Olivier HarismendyAssociate Professor of Medicine + BISB; cancer genomics + ML for biomarker discoveryMed + BISB
Vineet BafnaProfessor CSE + HDSI + BISB; computational genomics + algorithms + ML for cancerCSE + HDSI + BISB
Nigel GoldenfeldChancellor's Distinguished Professor of Physics + BioE affiliated (recently moved from UIUC); NAS Member; statistical physics + biology + universal scalingPhysics + BioE, NAS
Melissa GymrekAssociate Professor of Medicine + CSE; genome-wide tandem repeat analysis + ML for genetic variationMed + CSE
Kun ZhangDepartment Chair + Professor of BioE; single-cell genomics + ML for cell typingBioE Chair
Chem
化学
MS in Chemistry & Biochemistry
MS in Materials Science & Engineering
无独立 AI×Chem 项目
Chem MS 罕见
Chem Top 15
USNews
 弱
仅个别课< 25%
 弱
无系统交叉
课程重合详情
Chem / MSE 中的 AI 元素
全部 重合 elective 独有
课号课程类型
NANO 134Computational Materials重合
CHEM 273Computational Chemistry重合
CSE 250AProbabilistic Reasoningelective
CHEM 174Quantum Mechanics仅 Chem
师资重合详情
化学 × ML 教师
全部 affiliated
姓名主要方向关系
Francesco PaesaniKurt Shuler Faculty Scholar + Professor Chemistry & Biochemistry + Materials Science + San Diego Supercomputer Center; quantum dynamics + machine learning for many-body water + neural network potentials (MB-pol)Chem + MSE + SDSC, Shuler Scholar
Andrew McCammonJoseph E. Mayer Chair in Theoretical Chemistry + Distinguished Professor; NAS Member + AAAS Fellow; computational biophysics + molecular dynamics + ML for drug discoveryChem, Mayer Chair, NAS
Joseph S. FranciscoPresident's Distinguished Professor of Chemistry & Biochemistry; NAS Member + AAAS Fellow; theoretical chemistry + atmospheric chemistry + ML for kineticsChem, NAS
Kim PratherDistinguished Professor of Chemistry + Scripps + Cecil H. and Ida M. Green Distinguished Chair in Marine Sciences; NAS Member + AAAS Fellow; atmospheric chemistry + ML for aerosol classificationChem + Scripps, NAS, Green Chair
Lingyan ShiAssociate Professor of NanoEngineering; biophotonics + ML for biological imagingNanoEng primary
Shaochen ChenDistinguished Professor + Department Chair NanoEngineering; NAI Fellow + AIMBE Fellow; nano-fabrication + ML for biomanufacturingNanoEng Chair, NAI Fellow
Liangfang ZhangJoan and Irwin Jacobs Endowed Chair + Professor of NanoEngineering + Moores Cancer Center; NAE Member + AIMBE Fellow; nanomedicine + ML for drug deliveryNanoEng, Jacobs Chair, NAE
David TirrellProfessor (visiting); biomolecular engineering + AAAS FellowChem visiting
Phys
物理
MS in Physics（罕见）
MS in Computational Science（CSME, 跨学科）
CSME 是计算科学硕士
Phys Top 25
估算
 中
CSME 接受物理≈ 40%
 中
部分跨界
课程重合详情
物理涉及 AI 的课
全部 重合 elective 独有
课号课程类型
PHYS 244Parallel Computing for Physics重合
PHYS 235ML for Physical Sciences重合（按年）
CSE 250AProbabilistic Reasoningelective
PHYS 217Quantum Mechanics仅 Phys
师资重合详情
Physics × ML 教师
全部 affiliated
姓名主要方向关系
Nigel GoldenfeldChancellor's Distinguished Professor of Physics (recently moved from UIUC); NAS Member + APS Fellow; statistical physics + biology + universal scaling + ML for complex systems; author of "Lectures on Phase Transitions"Physics, NAS Chancellor Chair
Javier DuarteAssociate Professor of Physics + HDSI; ML for particle physics + LHC + CMS experiment + 2025 Particle Data Group ML chapter co-authorPhysics + HDSI
Tatyana SharpeeProfessor Salk Institute + UCSD Physics affiliated + Math; computational neuroscience + statistical ML for sensory codingSalk + Physics + Math
Brian KeatingChancellor's Distinguished Professor of Physics; cosmology + CMB + Simons Observatory co-PI + ML for B-mode detectionPhysics, Chancellor Chair
Daniel ArovasDistinguished Professor of Physics; condensed matter theory + topological phases + ML for quantum systems; APS FellowPhysics, APS Fellow
Marios GalanisSenior Researcher Physics; quantum many-body + ML methods for tensor networksPhysics primary
Massimiliano Di VentraDistinguished Professor of Physics; computational neuroscience + memcomputing + ML hardwarePhysics primary
John McGreevyProfessor of Physics; theoretical physics + holography + entanglement + ML applicationsPhysics primary
Patrick DiamondDistinguished Professor of Physics; plasma physics + nonlinear dynamics + ML for fusion; APS FellowPhysics, APS Fellow
Biz
商科
MS in Business Analytics (Rady)
MS in Finance
Rady MBA + Tech specialization
Rady MSBA 含强 ML
Rady MBA #45
USNews
 中
MSBA 含 ML≈ 50%
 中
部分共聘
课程重合详情
Rady MSBA 中的 AI 课
全部 重合 elective 独有
课号课程类型
MGT 458Predictive Analytics重合（MSBA 必修）
MGT 471Data Analytics for Decision Making重合
MGT 451Business Statistics重合
CSE 250AProbabilistic Reasoningelective
DSC 240MLelective
MGT 433Strategy仅 MBA
师资重合详情
Rady × CSE 教师
全部 primary affiliated
姓名主要方向关系
Lisa OrdóñezStanley and Pauline Foster Endowed Dean Rady School of Management; behavioral decision-making + judgment + Rady MSBA leaderRady Dean, Foster Chair
Vincent NijsAssociate Dean for Master's Programs + Professor of Marketing; marketing analytics + ML for consumer behavior + Radiant (R-based open-source analytics)Rady Assoc Dean, MSBA primary
Amit BhattacharjeeAssociate Professor of Marketing; consumer behavior + ML for prosocial behaviorRady Mktg
Gordon Burtch(formerly Boston U Questrom; recently affiliated); ML × digital platformsRady Mktg primary
Yuyu FanLecturer + MSBA Faculty; machine learning + Big Data analyticsRady MSBA primary
Terrence AugustProfessor of Innovation, Technology, and Operations; cybersecurity economics + ML for software securityRady ITO
Wendy LiuAssociate Professor of Marketing; consumer psychology + ML for recommender systemsRady Mktg
On AmirProfessor of Marketing + (former) Department Chair; consumer behavior + ML for choiceRady Mktg
Joe PancrasAssociate Professor + Faculty Director MSBA; marketing analytics + customer relationship ML + database marketingRady MSBA Director
Charles SprengerProfessor of Economics and Strategic Management; behavioral economics + experimental designRady Econ/Strategy
Jim AndrewsLecturer MSBA; data engineering + analytics for businessRady MSBA primary
Ulrike SchaedeProfessor of Japanese Business + JFIT Director; international business + Japan business intelligenceRady IJB primary

UCSD 是 UC 系统中 AI 资源仅次于 Berkeley 的学校，且 HDSI MSDS 项目对所有 quantitative 背景明文友好。BISB（生物信息学）是西海岸顶级。最佳路径：(1) 数据/任意理工科背景 → HDSI MSDS（在线版本对工作专业人士尤友好）；(2) 生物背景 → BISB；(3) 商科背景 → Rady MSBA（次于 McCombs/Sloan）。化学/物理交叉项目较弱。

来源：cs.ucsd.edu · datascience.ucsd.edu · bioinformatics.ucsd.edu · rady.ucsd.edu
12

University of Michigan, Ann Arbor

密歇根大学安娜堡分校 · CSE / EECS · MIDAS · UMSI iSchool #2 · MDS（Stat 主导跨 EECS+Biostat）+ MADS（UMSI 在线）
USNews CS #11 (CS)
①

AI program 核心专业课 & Listed Faculty

UMich 的 AI 资源横跨 EECS（CSE 部分）、UMSI（信息学院, USNews iSchool #2）、LSA Statistics、MIDAS（Michigan Inst for Data Science）、DCMB（CompMed & Bioinformatics, 2024 年改名）。研究生项目验证：MS in CS、MAS、MDS（Stat 系运行）、MBAn（Ross）、MSI / MADS（UMSI）、MS in Bioinformatics（DCMB）、AIM MS、QFRM MS。

EECS 445Introduction to Machine Learning（本科）
EECS 453Principles of Machine Learning
EECS 545Machine Learning（CSE grad, Honglak Lee）
EECS 553Machine Learning（ECE grad, SIPML）
EECS 504Foundations of Computer Vision
EECS 542Advanced Topics in Computer Vision
EECS 448Applied ML for Modeling Human Behavior
EECS 449Conversational AI
ECE 505Computational Data Science
ECE 551Matrix Methods for Signal Processing & ML
ECE 559Optimization Methods for SIPML
SI 670Applied Machine Learning（UMSI）
SI 630NLP: Algorithms and People（UMSI）
STATS 503Statistical Learning II
BIOSTAT 626Machine Learning Methods
BIOINF 535Generative AI in Biomedical Research（DCMB）

Listed Faculty（节选）：

Honglak Lee Justin Johnson Joyce Chai Jenna Wiens Lu Wang Rada Mihalcea Long Nguyen Yuekai Sun Ambuj Tewari Qiaozhu Mei Paul Resnick Chris Brooks

EECS 445 / 453 / 545 / 553 互斥（cannot take more than one for credit, 官方明文）。Long Nguyen / Ambuj Tewari 是 Stat primary + EECS by courtesy。Honglak Lee 同时担任 LG AI Research 首席科学家。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

UMich 跨学院选课结构非常成熟。独特路径：MIDAS Graduate Data Science Certificate 明确 "open to all U-M graduate students from any field"——9 学分课 + 3 学分 practicum, 含 EECS 409 seminar。MDS 必修课明文 cross-list 到 EECS 402/403, 表明跨系结构是制度化的, 而不仅是个案。

B · 学位计算

Stat / Math / Bioinformatics / Ross / UMSI master 学生可以将 EECS 545 算入 elective（多数项目允许；MBAn 学生需 override）。MDS 把 EECS 545/553/SI 670/BIOSTAT 626 直接列为 ML approved core——这是常春藤外最完整的"非 CS master 拿 EECS ML 课"的官方路径。MIDAS Certificate 可与任何 UMich master 叠加。

来源：bulletin.engin.umich.edu/courses/eecs · midas.umich.edu/training/students/graduate-data-science-certificate-program · lsa.umich.edu/stats/masters_students · si.umich.edu · medschool.umich.edu/departments/computational-medicine-bioinformatics
③

与 AI 交叉的硕士项目（6 领域）

UMich × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MAS · Master's in Applied Statistics（LSA Stat 系, 30 学分）
MDS · Master's in Data Science（LSA Stat 系主导, 跨 EECS+Biostat, Yuekai Sun 任 Director）
MDS 由 Stat 系运行
Stat Top 5
USNews
 强
MDS 必修含 EECS 545/553约 70%
 强
Stat ↔ EECS by-courtesy 联合
课程重合详情
MDS / MAS 与 EECS AI 课的重合（MDS 项目页面列出）
全部 重合 等价 独有
课号课程类型
STATS 500Statistical Learning I: RegressionMAS 必修
DATASCI 503Statistical Learning II: MultivariateMAS 必修
STATS 510 / 511Probability / Stat InferenceMAS 必修
MATH 403 / DATASCI 403Discrete MathematicsMDS 核心
EECS 402Programming for Scientists and EngineersMDS 核心
EECS 403Graduate Foundations of DS & AlgorithmsMDS 核心
EECS 545Machine Learning（CSE 版, Honglak Lee）MDS approved ML
EECS 553Machine Learning（ECE 版）MDS approved ML
SI 670Applied Machine LearningMDS approved ML
BIOSTAT 626Machine Learning MethodsMDS approved ML
DATASCI 551Bayesian Modeling and ComputationMDS approved ML
STATS 600Linear Models仅 Stat

UCSD Department of Mathematics开 MA Math + MAS in Statistics + MAS in Applied Math + MAS in Computational Science—UCSD Math 是少数 4 个 MAS（Master of Advanced Study, terminal master）轨同时开设的系。AI/ML 主要桥接 HDSI（Saab, Cloninger 双 affiliations）+ Center for Computational Math (CCoM, by Melvin Leok director, SIAM Fellow)。Bill Helton (SIAM+AMS Fellow Emeritus) 是 SDP + ML 理论奠基者。

师资重合详情
Stat 系内含 by-courtesy EECS 教授
全部 joint primary
姓名主要方向关系
Long NguyenBayesian Mixture Models, Topic ModelsStat primary + EECS by courtesy
Yuekai SunStatistical ML, AI fairness, LLM evaluation; MDS founding directorStat primary + MIDAS directory
Ambuj TewariRL, Online LearningStat primary + EECS by courtesy
Liza LevinaStatistical Network Analysis, High-dim Stats; 前 Stat 系 Chair (2020-2025)Stat primary + MIDAS affiliated
Ji ZhuStatistical ML, Network Analysis; 现任 Stat 系 ChairStat primary + MIDAS directory
Alfred HeroStatistical ML, signal processing, data science; MIDAS Co-Director; John H. Holland Distinguished University ProfessorEECS primary + Stat secondary + BME + CCMB + MIDAS Co-Director
Honglak LeeDeep Learning, Representation Learning; LG AI Research Chief ScientistCSE + AI Lab
Jenna WiensML for Healthcare; AI Lab Associate DirectorCSE + AI Lab
Math
数学
AIM · Applied & Interdisciplinary Mathematics MS（31 学分）
Applied Math MS（与 AIM 不同）
QFRM · Quantitative Finance & Risk Management MS（Math 系 + Stat 系联合, 36 学分, Erhan Bayraktar 任 Director）
AIM + QFRM 都允许跨系 elective
Math Top 10
USNews
 中
AIM 允许 elective 跨系约 50%
 中
Math 共聘较少, AIM 跨系机制为正式
课程重合详情
AIM / QFRM elective 中含 AI 的课
全部 重合 elective 独有
课号课程类型
MATH 571Numerical Methods for Sci Computing IAIM 推荐
MATH 572Numerical Methods II（Differential Equations）AIM 推荐
MATH 561Linear Programming I优化, AIM elective
MATH 564Continuous Optimization优化, AIM elective
MATH 656Introduction to PDEAIM 核心
EECS 545Machine LearningAIM elective（按个案）
STATS 600Linear ModelsAIM elective
MATH 593Algebra仅 Math

AIM PhD 设计上要求"co-advisor from outside Math"——是 UMich 数学系最支持跨学科研究的项目。AIM MS 31 学分中 9 门课 + Math 501 seminar，elective 灵活。QFRM 是 Math+Stat 联合, 替代了原 MFE 项目（2015 起）。AIM MS 主要面向 AIM PhD 同学和 MLB Scholars，普通 master 申请者建议申 Applied Math MS。

师资重合详情
Math 系内的 AI/ML 教师
全部 joint
姓名主要方向关系
Erhan BayraktarMathematical Finance, mean field games, ML; QFRM DirectorMath primary + MIDAS directory
Zhiyan DingNumerical analysis for ML, mean-field analysis, deep learning theory（2025 秋新入职）Math primary + MIDAS Affiliated + Quantum RI

UCSD 生物 AI 主体 = Shu Chien-Gene Lay Department of Bioengineering（Jacobs SoE 旗下, 由 Y.C. Fung 1966 创立）+ Bioinformatics and Systems Biology (BISB) graduate program（NIH 训练 grant 资助跨系 PhD/MS, founding director Shankar Subramaniam）+ Department of Cellular and Molecular Medicine。Knight (NAS, Wolfe Chair, Center for Microbiome Innovation Director) 是微生物组 AI 国际旗手; Palsson (NAE, Galetti+Fung Chair) 是 systems biology + iModulons 创始者; Pevzner (NAE, Taylor Chair) 是 computational genomics 国际权威。Goldenfeld 已从 UIUC 转 UCSD 物理（NAS, statistical physics × biology）。
Bio
生物
MS in Bioinformatics（DCMB, 30 学分, 必有 research presentation）
MS in Biostatistics（SPH）
BIDS-TP（NIH T32 训练计划, DCMB+MIDAS 联合）
DCMB 是 NIH T32 训练点
Bio Top / Biostat #4
USNews
 强
DCMB 课程含 ML约 65%
 强
CCMB 140+ faculty 跨 6 学院
课程重合详情
Bioinformatics MS 课程与 EECS 545 / SI 670 重合
全部 重合 elective
课号课程类型
BIOINF 527Intro to Bioinformatics & Computational BiologyMS 核心
BIOINF 529Bioinformatics Concepts & AlgorithmsMS 核心可选
BIOINF 575Programming Lab in BioinformaticsMS 核心可选
BIOINF 531Probability and Applied Stats for BioinformaticsMS Prob/Stat 选项
BIOINF 535Generative AI in Biomedical Research新, 直接 AI
BIOSTAT 521 / 522Probability / Statistical Inference for BiostatMS Prob/Stat 选项
BIOSTAT 626Machine Learning MethodsMS Biostat 核心
EECS 545Machine LearningMS elective（advanced）

DCMB 已正式更名为 Gilbert S. Omenn Department of Computational Medicine and Bioinformatics（2024 年 Omenn 夫妇 $25M 捐赠后）。Chair 为 Brian D Athey。CCMB 是其下中心，140+ faculty 跨 6 学院（Med / Engineering / LSA / Pharmacy / Public Health / UMSI）。DCMB 官方 BGP 培训计划列出 ML/omics predictions track 的代表 PI：Guan, J. Liu, Ye, Baladandayuthapani, Kretzler, Sartor, Dinov, Hero, Draelos, Au。BIDS-TP 是 NIH T32 训练计划（DCMB + MIDAS 合办）。

师资重合详情
CCMB faculty 来自 6 个学院
全部 joint primary
姓名主要方向关系
Maureen SartorCancer epigenomics, multi-omics, prediction methods; Co-Director Bioinformatics Grad ProgramDCMB + Biostat 联合 + MIDAS directory
Joshua WelchGenerative AI（autoencoders, diffusion models, GANs）for single-cell genomicsDCMB + EECS/CSE 联合 appointment
Jie LiuML in computational biology; co-instructs BIOINF 593/EECS 598DCMB + EECS Associate Prof
Yuanfang GuanML in biology and medicine; multiple Kaggle Data Science Bowl gold medalsDCMB + Internal Medicine + MICDE
Arvind RaoML/statistical modeling for radiomics, digital pathology, AI predictive models; AAAS Fellow 2025DCMB + BME + Radiation Oncology + Biostat + MICDE
Alfred HeroStatistical ML, signal processing, bioinformatics; MIDAS Co-DirectorEECS + BME + Stat + CCMB + MIDAS Co-Director
Margit BurmeisterBrain disorder genetics; Associate Chair DCMB; Bioinformatics Grad Program DirectorDCMB + Director（lab 非 ML 主线）
Alla KarnovskyBioinformatics MS Program Director; metabolomics computational methodsDCMB Associate Professor
Jenna WiensML for Healthcare; AI Lab Associate Director（CCMB affiliated）EECS primary, CCMB affiliated

UCSD Department of Chemistry & Biochemistry（in Division of Physical Sciences, Urey Hall）+ Department of NanoEngineering（Jacobs SoE）共同构成化学 AI 集群。Paesani (Shuler Scholar, MB-pol 多体水 ML 势能函数开发者) 是 ML × 量子化学旗舰; McCammon (NAS, Mayer Chair) 是分子动力学 + drug design 鼻祖; Francisco (NAS) + Prather (NAS, Green Chair) 共三位 NAS 院士驻 Chem。Liangfang Zhang (NAE, Jacobs Chair) 在 NanoEng 主导 ML × 纳米医学。化学 master 走 MS Chemistry 或跨系 MS NanoEngineering。
Chem
化学
MS in Chemistry（LSA Chem 系, 9 个月, course-based 或 research-based）
专业方向：Analytical / Inorganic / Organic / Chemical Biology / Materials
9 月制 MS 存在
Chem Top 15
USNews
 弱
Chem MS 必修课无 ML< 25%
 弱
Chem 系内无 EECS 共聘
课程重合详情
Chem MS 课程与 AI 几乎无重合
全部 独有
课号课程类型
CHEM 540Organic PrinciplesOrganic 必修
CHEM 507Advanced Inorganic ChemistryInorganic 必修
CHEM 511Materials ChemistryMaterials 必修
CHEM 535Physical Chemistry of MacromoleculesMaterials elective
CHEM 596Research（research-based）MS research 选项

UCSD Department of Physics（in Division of Physical Sciences, Mayer Hall）AI/ML 集中在: (1) statistical physics + biology（Goldenfeld 已 2023 由 UIUC 转 UCSD, NAS Chancellor Chair）; (2) particle physics ML（Duarte CMS + LHC, 双 HDSI）; (3) cosmology ML（Keating Simons Observatory, Chancellor Chair）; (4) condensed matter + quantum（Arovas, McGreevy）; (5) plasma fusion ML（Diamond）。物理 master 走 MS Physics（UCSD 物理是 USNews top 15 物理 grad program）。

师资重合详情
Chem 系教师无 ML 主线
全部
姓名主要方向关系

UCSD Rady School of Management（2003 创立, by founding dean Robert Sullivan）开 MS in Business Analytics (MSBA, 11-month, STEM-designated, Full-Time + FlexMSBA part-time)。FlexMSBA 是 Southern California 唯一同类 part-time program。MSBA 课程深度 AI 集成: 课程涵盖 Big Data + advanced analytics + machine learning, AI-assisted curriculum 在每个环节集成 LLM (problem definition + data cleaning + coding + analysis + presentation)。Rady 历史 16 位 Nobel Laureates 校友/教师 + 8 位 MacArthur recipients。Vincent Nijs (Assoc Dean for Master's) 是 Radiant 开源分析平台开发者; Joe Pancras (MSBA Faculty Director) 主导 marketing analytics × ML。
Phys
物理
PHYS 系无独立 MS 项目（PhD only, 仅 PhD 中途可拿 MS）
Applied Physics 也是 PhD 主导
非物理背景拿 ML 的官方路径：MIDAS Graduate DS Certificate（9+3 学分, 任何 UM grad 可申）
无独立 Phys MS, 仅 MIDAS
Phys Top 10
USNews
 弱
无 Phys MS—
 中
MIDAS 跨学院, EECS 409 必修
课程重合详情
物理 PhD 课程中含 AI 的通常仅 elective
全部 重合 elective
课号课程类型
PHYSICS 514Computational Physics物理 PhD elective
EECS 409MIDAS Seminar Series（必修若拿 Certificate）MIDAS Certificate
EECS 545Machine LearningMIDAS elective
SI 670Applied MLMIDAS elective

UMich 物理系官方页面明确："The department does not have a stand alone Master of Science program; students can only be admitted to a PhD program."——所以"物理 master 转 AI"在 UMich 不是直接路径，需走 MIDAS Certificate（任何 UM grad 都可申，9 学分 + 3 学分 practicum）。

师资重合详情
MIDAS 通过 EECS 409 seminar 跨学院
全部 joint primary
姓名主要方向关系
Christopher MillerCosmology with ML, astroinformatics; AI/ML in astrophysics 先驱Astronomy + Physics + MIDAS directory
August (Gus) EvrardComputational cosmology, Dark Energy Survey, large-scale structure simulation; ARC Associate DirectorPhysics + Astronomy + MCTP + MIDAS Affiliated
Mark NewmanNetwork Science, complex systems; community detection / link prediction（JMLR 2023）Physics + CSCS（非 AI institute）

Christopher Miller 三条全满足（Astro+Phys + MIDAS + lab 明列 ML 算法）。Gus Evrard 是 Physics+Astronomy + MIDAS Affiliated + ARC Associate Director（计算宇宙学）, lab 用 N-body 模拟而非 ML 主线但与 Dark Energy Survey ML 工作流深度绑定，2/3 保留。Mark Newman 物理系 + CSCS 但 CSCS 是 Complex Systems Center 非 AI institute，2023 年 JMLR 论文表明 ML 方向，2/3 保留。
Biz
商科
MBAn · Master of Business Analytics（Ross, 36.5 学分, 10 个月, 2022 启动, STEM）
MSI · Master of Science in Information（UMSI）
MADS · Master of Applied Data Science（UMSI 在线, 38 学分, SIADS 前缀）
Ross MBAn + UMSI MADS 双线
UMSI iSchool #2
USNews
 强
MBAn / MADS 含完整 ML 主线约 70%
 强
UMSI 与 EECS by-courtesy 联合
课程重合详情
Ross MBAn 必修课 + UMSI MADS（SIADS）课程
全部 重合 elective
课号课程类型
Ross MBAn 核心：Predictive Analytics—MBAn 必修
Ross MBAn 核心：Causal Inference through Experimentation—MBAn 必修
Ross MBAn 核心：Unsupervised Learning—MBAn 必修
Ross MBAn 核心：Prescriptive Analytics—MBAn 必修
SI 670Applied Machine LearningUMSI MSI/MADS 核心
SI 671Data Mining: Methods and ApplicationsUMSI 核心
SI 630NLP: Algorithms and PeopleUMSI
TO 412Predictive Analytics（Ross 本科, 也开 grad）Ross
EECS 545Machine LearningMBAn 学生需 override

UMSI（School of Information）USNews iSchool 排名 #2。MADS（Master of Applied Data Science）是完全在线 38 学分项目, 1 月一门课的模块化设计，由 Qiaozhu Mei 创办；2022 年起 academic director 由 Kevyn Collins-Thompson 接任，Mei 升任 UMSI Associate Dean for Research。UMSI 的 AI research 页面列出 ML 方向 PI 包括 Mei / Jurgens / Collins-Thompson / Mihalcea（也在 EECS）/ Budak 等。Ross MBAn 是 STEM 一年制 36.5 学分项目, 2022 启动。

师资重合详情
Qiaozhu Mei 等 UMSI 教师有 EECS by-courtesy
全部 joint primary
姓名主要方向关系
Qiaozhu MeiIR, text mining, ML; MADS founding director（2019-2022）；UMSI Associate Dean for ResearchUMSI + EECS courtesy + MIDAS directory
Kevyn Collins-ThompsonMADS academic director（2022 起）；ML in education / IRUMSI + EECS courtesy
David JurgensNLP, AI, computational social scienceUMSI + EECS courtesy
Christopher BrooksData Science, Learning Analytics; UMSI Associate Dean for Academic AffairsUMSI
Paul ResnickHCI, Recommender SystemsUMSI

Mei / Collins-Thompson / Jurgens 三人都同时持有 EECS courtesy appointment + UMSI 主聘 + lab/research 明列 ML/NLP（3/3）。Resnick 在 UMSI 但不在 MIDAS 当前 directory；recommender systems 是经典 ML 应用（2/3）。Christopher Brooks 是 UMSI Associate Dean for Academic Affairs（2/3 保留）, 但他不是 MADS academic director（该职 2022 后由 Collins-Thompson 担任）。Ross MBAn 教师列表未在公开页面以 ML 标签明确呈现，无新增条目。

UMich 是 Tier 2 中跨学院结构最成熟的之一。MDS（Stat 主导跨 EECS+Biostat）+ MADS（UMSI 在线）+ MIDAS Certificate 三条路径覆盖了从全日制研究型到在职专业型的全部需求。最佳路径：(1) 数据 / 工程背景 → MDS（Stat 系，Yuekai Sun 任 Director）；(2) 任何 STEM grad → MIDAS Certificate；(3) 在职 / 非 STEM → MADS（UMSI 在线 38 学分）；(4) 生医 → DCMB Bioinformatics MS（必有 research, 30 学分）；(5) 数学 → AIM MS 或 QFRM；(6) 商科 → Ross MBAn。注意：物理系无独立 MS, 化学 MS 只有 9 月制且无 ML 课——这两条线必须经 MIDAS Certificate 才能桥接。

来源：bulletin.engin.umich.edu · midas.umich.edu · lsa.umich.edu/stats · lsa.umich.edu/math · lsa.umich.edu/physics · lsa.umich.edu/chem · sites.lsa.umich.edu/quant · medschool.umich.edu/departments/computational-medicine-bioinformatics · si.umich.edu · michiganross.umich.edu
13

University of Maryland, College Park

马里兰大学帕克分校 · UMIACS · CBCB · Center for Machine Learning · Science Academy（MSML/DATA/BIOI/MSAI cross-list 课程包）· 毗邻 NIH/NIST
USNews CS #15 (CS)
①

AI program 核心专业课 & Listed Faculty

UMD 的 AI 资源以 UMIACS（Inst for Adv Computer Studies, Mihai Pop 任 Director）、Center for Machine Learning（与 Capital One 合作）、CBCB（CompBio 中心, Mihai Pop 任 Director）为核心。研究生层面验证：MS in CS（thesis/non-thesis, 30 学分）、Science Academy 旗下的 SAML（MS in Applied ML）/ SADS（MS in Data Science）/ MPDA（MPS in DS & Analytics）/ MPML（MPS in ML）——这些项目全部 30 学分, 在职专业型, 晚间授课, 课程通过 MSML/DATA/BIOI/MSAI/MSQC 系列共享（cross-listed）。

CMSC 320Intro to Data Science（本科）
CMSC 421Intro to AI（本科）
CMSC 422Intro to Machine Learning（本科）
CMSC 470Intro to NLP
CMSC 471 / 472 / 473 / 474Intro to DV / DL / Capstone in ML / Game Theory
CMSC 723Computational Linguistics I（grad NLP）
CMSC 726Machine Learning（grad）
CMSC 720Special Topics in Machine Learning
CMSC 727Neural Modeling
CMSC 733Computer Processing of Pictorial Information
CMSC 740Advanced Computer Graphics
MSML 601Probability and Statistics（cross-list DATA/BIOI/MSAI 601）
MSML 602Principles of Data Science（cross-list）
MSML 603Principles of Machine Learning（cross-list）
MSML 604Optimization for ML
MSML 605Computing Systems for ML
MSML 606Algorithms & Data Structures for ML
MSML 612Deep Learning
MSML 640Computer Vision
MSML 641Natural Language Processing
MSML 642Robotics
MSML 650Cloud Computing
MSML 651Big Data Analytics

Listed Faculty（节选）：

Hal Daumé III Tom Goldstein Furong Huang Soheil Feizi Mihai Pop Aravind Srinivasan Pratap Tokekar Dinesh Manocha Rama Chellappa Heng Huang Jordan Boyd-Graber Marine Carpuat Philip Resnik Hector Corrada Bravo Andrew Childs

Center for Machine Learning（ml.umd.edu）由 UMIACS + CMNS + Capital One 联合资助。Mihai Pop 同时是 UMIACS Director、CBCB Director、CS Professor。Furong Huang 在 CS、UMIACS、Center for ML、AMSC 都有 appointment。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

UMD CMSC 系对外系研究生较开放。独特设计：Science Academy 是 CMNS 学院专门为"在职专业型 master"设立的执行单元, 提供 7 个 30 学分项目（SAML/SADS/MPDA/MPML/BIOI/MSAI/MSQC）, 全部在 College Park 校区晚间面授, 课程通过 MSML/DATA 等前缀 cross-listed。这意味着 1 套 ML 课程基础设施支撑多个 master degree, 是该校核心创新。

B · 学位计算

AMSC / Math / Stat / CBCB / Smith / iSchool master 学生可以将 CMSC 422 / 726 算入 elective。SAML / MPML 是 UMD 给"非 CS 转 ML 在职"的旗舰答案——10 门课全部以 MSML 前缀, 可在傍晚或晚间上课, 与 CMNS Science Academy 其他项目共享课程基础设施。

来源：cs.umd.edu/people/faculty · cmns.umd.edu/graduate/science-academy/machine-learning · academiccatalog.umd.edu/graduate/programs/applied-machine-learning-saml · ml.umd.edu · umiacs.umd.edu · cbcb.umd.edu · amsc.umd.edu
③

与 AI 交叉的硕士项目（6 领域）

UMD × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
AMSC · MS in Applied Mathematics, Statistics & Scientific Computation（跨学科, Math+CS+ECE+Stat 共同管理）
注：UMD 没有独立的 Statistics 系（统计放在 Math 系下）
AMSC 是跨学科
统计无独立排名
—
 中
AMSC 含 ML elective约 50%
 中
Furong Huang AMSC + CS 共聘
课程重合详情
AMSC / Stat 与 CMSC AI 课的重合
全部 重合 elective 独有
课号课程类型
AMSC 660Scientific Computing IAMSC 必修核心
AMSC 808NNumerical OptimizationAMSC 必修核心
AMSC 612Numerical Linear AlgebraAMSC 必修
CMSC 726Machine Learning（grad）AMSC elective
CMSC 720Special Topics in Machine LearningAMSC elective
CMSC 723Computational Linguistics（NLP）AMSC elective
STAT 700Mathematical Statistics（核心）仅 Stat

UMD 没有独立的 Statistics 系（与多数顶尖校不同）。Statistics Program 设在 Math 系下, 由 Lizhen Lin（2023 起）任 Program Director。AMSC（Applied Mathematics, Statistics & Scientific Computation）是跨学科 PhD/MS 项目, 包含 Stat concentration, 教师列表明文含 Furong Huang（CS+CMM）等 ML 学者, 是 Stat 学生通往 AI 学系的关键桥梁。

师资重合详情
AMSC ↔ CS 共聘
全部 joint primary
姓名主要方向关系
Lizhen LinStatistical foundations of deep neural networks, generative modeling, geometric deep learning; Stat Program DirectorMath/Stat + AIM listed faculty
Yun YangHigh-dim statistics, statistical learning theory, ML, diffusion modelsMath/Stat + AMSC faculty
Vince LyzinskiStatistical ML, random graph inference, JMLR papersMath/Stat
Furong HuangTrustworthy ML, RL, latent variable models（AMSC cross-listed, 接 Math/Stat 学生）CS primary + UMIACS + CMM core + AMSC affiliated

经三条标准（dept appointment + AI institute + lab AI keywords）筛查：Lizhen Lin 三条全满足（Stat Program Director + AIM 教师列表 + lab/blog 明列 deep neural network theory + generative modeling + geometric deep learning）。Yun Yang 和 Vince Lyzinski 是 2/3（Math/Stat 主聘 + lab 明确 ML 但未在 UMIACS/AIM/CMM 公开 directory）。Furong Huang 是 AMSC 跨列, 通过 AMSC 项目接 Math/Stat 学生。被删除（lab 无 AI 关键词）：Lahiri（survey sampling）、J. Ren（survival analysis）、Saegusa（semiparametric）、Slud（sample surveys）。
Math
数学
MA / MS in Mathematics
AMSC · MS in Applied Math, Stats & Scientific Computation（跨学科）
MS in Mathematical Finance（与 Smith 商学院联合）
AMSC 是数学转 AI 入口
Math Top 20
估算
 中
AMSC + CMSC elective约 50%
 中
部分跨界
课程重合详情
AMSC / Math 中含 AI 的课
全部 重合 elective
课号课程类型
AMSC 660Scientific Computing IAMSC 必修核心
AMSC 808NNumerical OptimizationAMSC 必修核心
AMSC 612Numerical Linear AlgebraAMSC 必修
AMSC 808FTopics in Computational StatisticsAMSC elective
CMSC 726Machine LearningAMSC elective
CMSC 720Adv MLAMSC elective
师资重合详情
数学 × ML 教师
全部 joint
姓名主要方向关系
Haizhao YangML for PDEs & Inverse Problems, deep learning theory, generative modelsMath primary + UMIACS affiliate + CMM core + AMSC
Deep RayNumerical analysis × Machine Learning, generative algorithms（wildfire, conservation laws）Math primary + IPST 联合 + CMM core
Furong HuangTrustworthy ML, RL（AMSC cross-listed, 接 Math 学生）CS primary + UMIACS + CMM core + AMSC affiliated

经三条标准筛查：UMD Math 系 faculty research interests 页面 21 位 faculty 中, 只有 D. Ray 和 Haizhao Yang 在 blurb 内出现"machine learning"。两人都是 CMM core faculty。Y. Yang（已在 Stat 域）也属于 Math 系。被删除（不满足三条）：Maria Cameron 虽然是 CMM core 但 lab 主页是 numerical methods for natural sciences, 不专做 ML（2/3）；Doron Levy 是 numerical analysis + math bio 但非 ML（1/3）。Furong Huang 通过 AMSC 项目对 Math 学生开放。
Bio
生物
MS in Bioinformatics & Computational Biology（CBCB, Mihai Pop 任 Director, CBCB 是 NIH 旗舰中心）
BIOI 系列课程跨多个 Science Academy 项目
CBCB 旗舰
Bio Top 25
估算
 强
CBCB 课程含 ML约 60%
 强
CBCB+CS 共聘
课程重合详情
Bioinformatics MS 与 CMSC AI 课的重合
全部 重合 elective
课号课程类型
BIOI 601Probability and Statistics for BioinformaticsMS 核心（cross-list MSML/DATA/MSAI）
BIOI 602Data Science PipelineMS 核心
BIOI 603Principles of Machine LearningMS 核心（cross-list MSML 603）
BIOI 612Deep LearningBIOI elective
CMSC 702Computational Biology重合
CMSC 798Topics in Bioinformaticselective
CMSC 726MLelective

UMD CBCB（Center for Bioinformatics & Computational Biology）是 UMIACS 旗下中心, 11 位 core faculty。CBCB Director 现任为 Michael Cummings（Biology + CS + UMIACS）, Mihai Pop 是 co-director（前 CBCB Director, 前 UMIACS Director 2018-2025）。CBCB 自己 about 页面说"strong focus on fundamental computational research in statistics and machine learning"。

师资重合详情
CBCB faculty 含 Mihai Pop（CS+CBCB）
全部 joint primary
姓名主要方向关系
Heng HuangML, AI, biomedical data science, computational neuroscience; Brendan Iribe Endowed Professor; CBCB faculty 直接标 "Machine Learning & Computational Health"CS primary + UMIACS + CBCB + CMM core
Michael CummingsBioinformatics + Machine Learning & Data Science（CS official 研究区 tag）；ML for Parkinson's disease (2024 publication); CBCB DirectorBiology + CS + UMIACS + CBCB Director
Mihai PopMicrobiome computational algorithms, metagenomics; 前 UMIACS Director (2018-2025); CBCB co-directorCS + UMIACS + CBCB + Microbiome Center co-director（lab 非 ML 主线）
Rob PatroBioinformatics algorithms, single-cell & bulk RNA-seq quantification (Salmon, alevin-fry)CS + UMIACS + ECE + CBCB（lab 非 ML 主线但工具用 ML）

经三条标准筛查 11 位 CBCB faculty：直接 AI keyword 命中的有 Heng Huang（research area tag 就是 "Machine Learning & Computational Health"）。Cummings 的 cs.umd.edu 页面研究区显式标 "Machine Learning and Data Science", 多篇 ML for Parkinson 论文。Pop 和 Patro 是 CS+UMIACS+CBCB 但 lab 不专做 ML，按 2/3 保留。被删除（lab 无 ML 关键词）：Colwell（global infectious diseases）、Najib El-Sayed（cancer biology）、Erin Molloy（phylogenetic inference, ML 接近但非主线）、Megan Fritz、Brantley Hall、Can Firtina、Jamshed Khan。Hector Corrada Bravo 已不在 UMD CBCB 当前列表（已转其他机构）。
Chem
化学
MS in Chemistry & Biochemistry（罕见）
无 AI×Chem 专门项目
UMD-NIST 合作（NIST 在 Gaithersburg 邻近）
Chem MS 罕见
Chem Top 30
估算
 弱
Chem MS 必修无 ML< 25%
 弱
无系统交叉
课程重合详情
Chem MS 必修与 AI 几乎无重合
全部
课号课程类型

UMD Chem MS 项目本身没有 AI 专门轨道, 但 Chemistry & Biochemistry 系内 Tiwary 实验室是名校级别的 AI×Chem PI（Generative AI for molecular dynamics）。UMD Physics 自己的 "AI and Physical Sciences" research area 也将 Tiwary 列为 6 位 AI+Physical Sciences 教师之一。

师资重合详情
Chem 系教师无 ML 主线
全部 joint
姓名主要方向关系
Pratyush TiwaryArtificial Chemical Intelligence@Maryland; AI + statistical mechanics for molecular dynamics; Generative AI / diffusion models for drug discoveryChemistry & Biochemistry + IPST + Institute for Health Computing + AMSC affiliated
Garegin PapoianTheoretical/computational physical chemistry; protein modeling; Amazon ML Research AwardChemistry & Biochemistry + IPST 联合（lab 非 ML 主线）

Pratyush Tiwary 三条全满足（Chem dept + IPST/IHC/AMSC + lab 名为 "Artificial Chemical Intelligence@Maryland"）。Papoian 是 Chem + IPST + 获 Amazon ML award 但 lab 主聚焦 theoretical biophysics, 非 ML 主线，2/3 保留。
Phys
物理
MS / PhD in Physics
JQI · Joint Quantum Institute（UMD-NIST 联合）
QuICS · Joint Center for Quantum Information & Computer Science（UMD-NIST）
JQI / QuICS 是 UMD-NIST 联合
Phys Top 15
USNews
 中
量子方向含 ML约 40%
 中
JQI / QuICS 跨学院
课程重合详情
量子+ML 类课程
全部 重合 独有
课号课程类型
CMSC 657Quantum ComputationCS+QuICS 联合开设
PHYS 622Quantum Mechanics物理必修, 与 ML 无直接关系

UMD Physics 自己有 "AI and Physical Sciences" research area 列出 6 位教师：Maissam Barkeshli, Victor Galitski, Michelle Girvan, Chris Jarzynski, Wolfgang Losert, Pratyush Tiwary。其中 Tiwary 已记 Chem 域。UMD-NIST 联合的 JQI 和 QuICS 是 Physics+CS 交叉的"量子+ML"方向。

师资重合详情
Andrew Childs 等 quantum computing faculty
全部 joint
姓名主要方向关系
Michelle GirvanNetwork science + ML, reservoir computing for chaos prediction; APS FellowPhysics + IPST + IREAP + AMSC + CMM core
Wolfgang LosertLiving systems × ML / DL; quantitative biophysics with deep learning; MPower Professor 2023Physics + IPST + IREAP + Maryland Biophysics + Bioengineering affiliate
Andrew ChildsQuantum Algorithms; QuICS Co-Director; quantum-classical hybrid MLCS primary + QuICS + JQI

Girvan 和 Losert 三条全满足（Physics 系 + 多个 institute 联合 + lab 主页明列 ML）。Andrew Childs 是 CS primary 但 QuICS 跨 Physics 系, 量子 ML 方向。UMD 物理系有非常强的 AI/ML 出口，与 UMich 物理系明显不同——这是 UMD 的相对优势。
Biz
商科
MS in Business Analytics（Smith 商学院, MSBA, 30 学分）
MS in Information Management（iSchool, MIM）
MS in Marketing Analytics（Smith）
Smith MBA + 多个分析方向
Smith MSBA + iSchool MIM
Smith MBA #20+
估算
 中
MSBA 含 ML约 50%
 中
部分共聘
课程重合详情
Smith MSBA + iSchool 中的 AI 课
全部 重合 elective
课号课程类型
BUDT 758TBig Data & AI for BusinessMSBA 核心（按年）
BUDT 758WData Mining & Predictive AnalyticsMSBA 核心
INST 633Data Analytics PracticumiSchool MIM 核心
INST 414Data Science TechniquesiSchool 核心
CMSC 726ML需 override

Smith 商学院 MS 项目已正式更名为 MS in Business Analytics & AI / MS in Information Systems & AI（项目名直接含 AI）, 由 DOIT (Decision, Operations and Information Technologies) 系承办。DOIT 系自己的 faculty page 把多位教授标记为 "AI Faculty"。Smith 2024 launched "All in on AI" initiative + new AI center。iSchool（USNews iSchool 排名 #1）的 MIM 学生可以选 AIM Director Hal Daumé III 的 NLP 课程作为 elective。

师资重合详情
Smith × CS 教师有限
全部 joint primary
姓名主要方向关系
Balaji PadmanabhanDean's Professor of DOIT; ML in business; pioneered ML in MBA at Wharton; Smith AI center leadSmith DOIT
Margret BjarnadottirOperations analytics, social impact AI; Smith DOIT "AI Faculty" 标签Smith DOIT (AI Faculty)
Jessica M. ClarkInformation Systems, AI; Smith DOIT "AI Faculty, Social Impact" 标签Smith DOIT (AI Faculty)
Manmohan AseriAI economics, algorithmic bias, fair advertising（2024 加入）Smith DOIT
Hal Daumé IIINLP / generative AI; AIM Director; cross-listed for iSchool MIM studentsCS primary + UMIACS + AIM Director

Smith DOIT 多位 faculty 直接标 "AI Faculty"。Padmanabhan 2023 加入是核心人物。Bjarnadottir 和 Clark 是 official AI Faculty 标签。Hal Daumé III 是 CS primary 但通过 AIM 跨 iSchool MIM 项目（他的 NLP/AI 是 UMD AI 研究最高级别之一）。这反映 UMD Smith 在商科 AI 方向有较系统的部署。

UMD 的核心优势是 地理位置（DC area, 毗邻 NIH/NIST）+ Science Academy 在职 master 矩阵。Hal Daumé III 是 NLP 顶级。最佳路径：(1) 在职转型 → SAML / MPML（晚间面授）；(2) 全日制研究 → MS in CS（thesis 选项）；(3) 生物医学 → CBCB Bioinformatics（毗邻 NIH 优势）；(4) 数学 / 统计 → AMSC（含 Stat concentration）；(5) 商科 → Smith MSBA。化学 MS 弱；物理 MS 通过 JQI/QuICS 量子方向有 ML 切入点（Andrew Childs）。

来源：cs.umd.edu · cmns.umd.edu · cbcb.umd.edu · amsc.umd.edu · rhsmith.umd.edu · ischool.umd.edu
14

Columbia University

哥伦比亚大学 · DSI（Data Science Institute）· 工程学院 SEAS · IEOR FE Top 1 quant · MA Stats 全美最大之一
USNews CS #9 (CS) · Top 5 (Stat)
①

AI program 核心专业课 & Listed Faculty

Columbia 的 AI 资源核心在 SEAS（工程学院）的 CS Department + DSI（Data Science Institute） + Stat Department（FAS） + IEOR。研究生层面验证：MS in CS（多个 track 包括 Machine Learning, Computational Biology, NLP）、MS in Data Science（DSI 主导, 30 学分, 6 home dept）、MA Stats（含 ML/AI track）、MS Financial Engineering（IEOR, Top 1 quant）、SPS Applied Analytics（兼职在线）。

COMS W4771Machine Learning（grad, Daniel Hsu）
COMS W4252Introduction to Computational Learning Theory
COMS W4995Topics: Applied ML / Applied DL
COMS W4705Natural Language Processing
COMS W4731Computer Vision I
COMS W4732Computer Vision II
COMS W4762Machine Learning for Functional Genomics
COMS W4761Computational Genomics
COMS W4773Machine Learning Theory
COMS W4773Algorithms for Massive Data
COMS W4121Computer Systems for DS（DSI MS 必修）
COMS W4721Machine Learning for DS（DSI MS 必修）
CSOR W4246Algorithms for DS（DSI MS 必修）
STAT W4701Exploratory Data Analysis & Visualization（DSI MS 必修）
STAT W4702Statistical Inference and Modeling（DSI MS 必修）
STAT GR5243Applied DS and AI
IEOR E4525ML for OR & FE
IEOR E4577ML for Time Series
IEOR E4730Practical DL System Performance
IEOR E4573Business Apps of LLMs

Listed Faculty（节选）：

David Blei Daniel Hsu Tony Jebara Liam Paninski Andrew Gelman Itsik Pe'er Smaranda Muresan Lydia Chilton Mohammed AlQuraishi Carl Vondrick Junfeng Yang Eugene Wu Jose Blanchet Garud Iyengar

David Blei 是 Stat + CS double primary（FAS Prof of Stat + Engineering Prof of CS）+ DSI member。Daniel Hsu 是 CS Associate Professor + DSI member。Mohammed AlQuraishi 是 2021 加入的蛋白质结构 ML 学者（Systems Biology）。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

DSI MS in Data Science 是跨 6 home dept 的官方互通路径（CS, Stat, IEOR, Bus, EE, App Math）。注册警告（官方原文）："Many departments, including DSI, give registration priority to their students. Space permitting, courses are then opened up to students outside the department."——意味着外系学生选 COMS W4771 等热门 ML 课需等待空位。

B · 学位计算

MA Stats 学生可以将 COMS W4721/W4771/W4995 算入"Approved Electives"（官方批准）。2025 起 MA Stats 新增 ML/AI 专门 track, STAT GR5243（Applied DS and AI）成为该 track 必修。MS-CS-CompBio 学生可以将 BIOL/BINF 课程作为 fundamental 必修, 是非 CS 背景生医研究者的潜在入口。

来源：datascience.columbia.edu/education/programs/m-s-in-data-science · stat.columbia.edu/masters-programs · ma.stat.columbia.edu/program-requirements · cs.columbia.edu/education/ms · gsas.columbia.edu/content/statistics-ma · ieor.columbia.edu
③

与 AI 交叉的硕士项目（6 领域）

Columbia × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MA in Statistics（Stat 系, 30 学分, 3 core + 6 electives + capstone）
含 4 个 tracks：Statistical ML/AI · Advanced ML · Risk & Financial Modeling · Mathematics of Finance
2025 起 Stat 也是 DSI MS in DS 的 home dept 之一
MA Stats 含 ML 专门 track
Stat Top 5
USNews
 强
MA Stats 含 ML/AI 专门 track约 70%
 强
David Blei = Stat + CS double primary
课程重合详情
MA Stat ML track 与 CS 课重合
全部 重合 等价
课号课程类型
STAT GR5203Probability Theory（核心）MA 必修核心
STAT GR5204Statistical Inference（核心）MA 必修核心
STAT GR5205Linear Regression Models（核心）MA 必修核心
STAT GR5243Applied Data Science and AIML/AI track 必修
STAT W4400Statistical Machine Learningelective
STAT GR5293Topics in Modern Statisticselective
COMS W4721Machine Learning for Data ScienceStat 学生可选
COMS W4771Machine Learning（grad, Daniel Hsu, "NOT a theory course"）Stat 学生可选
COMS W4995Topics: Applied ML / Applied DLStat 学生可选

Columbia 是公认的 ML×Statistics 重镇。MA in Statistics 含 ML/AI 专门 track（2025 起）。Stat 系自己维护 stat.columbia.edu/department-directory/faculty 列表, 27 位 senior faculty 中 13 位的 research interests 直接含 ML/Bayesian/data science 关键词。

师资重合详情
David Blei 等双系 primary 教授
全部 joint primary
姓名主要方向关系
David BleiProbabilistic ML, Bayesian Stats; JMLR Editor-in-Chief; ACM Fellow, IMS Fellow, Guggenheim, Simons InvestigatorStat + CS double primary + DSI member
Liam PaninskiStatistical ML for neural data analysis; Center for Theoretical Neuroscience + Grossman Center co-director + Kavli Institute memberStat + Neuroscience + Center for Theoretical Neuroscience + Grossman Center
John CunninghamStatistical machine learning, neuroscience applicationsStat dept primary
Andrew GelmanBayesian statistics, social/political scienceStat + Political Science (Higgins Professor)
Samory KpotufeStatistical ML theory, nonparametric methods, transfer learning; Stat Vice ChairStat dept Vice Chair
Tian ZhengML methods for complex networks and biology, data science applicationsStat dept + DSI
Marco Avella MedinaRobust statistics, ML, optimizationStat dept
Cynthia RushStatistical ML, approximate message passing, high-dim statsStat dept
Bianca DumitrascuComputational biology + MLStat dept
Ming YuanHigh-dimensional statistics, ML, optimizationStat dept
Arian MalekiCompressed sensing, high-dim stats, MLStat dept
Christopher HarshawCausal inference, randomization, MLStat dept

经三条标准（dept + AI institute + lab）筛查 Columbia Stat 27 位 senior faculty：13 位通过 AI keyword 过滤（含 Blei、Paninski、Cunningham、Kpotufe、Tian Zheng、Avella、Rush、Dumitrascu、Yuan、Maleki、Gelman、Harshaw、Lo）。**David Blei 全 3/3**：Stat+CS 双系 primary + DSI member + lab/CV 主页明列 ML 为核心。Paninski 也 3/3：Stat dept + Center for Theoretical Neuroscience/Grossman Center co-director + lab 明确 ML for neural data。被删除：Karatzas、Protter、Davis、de la Pena、Mukherjee、Rabinowitz、Sobel、Sen、Liu、Nutz、Tavare、van Delft、Ying（lab 主页无明显 AI keyword）。
Math
数学
MA in Mathematics（数学系）
MA in Mathematics with Specialization in the Mathematics of Finance（与 Stat 系联合）
FE · MS in Financial Engineering（IEOR, 顶级 #1 quant program）
IEOR FE Top 1 quant
Math Top 10
USNews
 中
IEOR FE 含 ML 重约 50%
 中
IEOR ↔ DSI 紧密
课程重合详情
IEOR FE 中的 ML 课
全部 重合 独有
课号课程类型
IEOR E4525Machine Learning for OR & FEFE elective
IEOR E4577Machine Learning for Time SeriesFE elective
IEOR E4571Personalization Theory and ApplicationFE elective
IEOR E4730Practical Deep Learning System PerformanceFE elective
IEOR E4731NLP for FinanceFE elective
IEOR E4573Business Applications of LLMs新（FE elective）
IEOR E4721Big Data in FinanceFE elective
MATH GR5320Differentiable Manifolds纯数学 elective

Columbia 的 Applied Math 在 SEAS 学院的 APAM (Applied Physics & Applied Mathematics) 系下, 不是 FAS Math 系。APAM Applied Math program 14 位 faculty 中, 4 位与 ML/data science 强相关。APAM 协调的 LEAP (Learning the Earth with Artificial Intelligence and Physics) 是 NSF Science and Technology Center (2021), 是 APAM 与 ML/AI 交叉的旗舰中心。Math 系（FAS）本身的 master 项目（MA in Mathematics）则更纯数学方向。

师资重合详情
IEOR 教师含 ML 应用
全部 joint primary
姓名主要方向关系
Chris H. WigginsApplied math + ML, mathematical biology, network inference; CNN head of ML and AI science; Business Insider AI 100APAM Applied Math + Systems Biology + DSI
Qiang DuNumerical analysis + scientific computation + data sciences and ML; AAA&S Fellow, Gordon Bell Prize finalist, AMS/SIAM/AAAS FellowAPAM Applied Math + DSI co-Chair Center for Foundations of Data Science + Center for Computing Systems for Data-Driven Science
Kui RenNumerical analysis × ML, data-driven inverse problems and learning; 2025 Feng Kang Prize, 2026 SIAM FellowAPAM Applied Math + DSI affiliate
Liliana BorceaWave propagation, inverse problems, data-driven reduced order modelingAPAM Applied Math
Daniel BienstockOptimization, integer programming; CBS DRO Chair joint appointmentAPAM + IEOR + Columbia Business School DRO Chair

经三条标准筛查 APAM Applied Math 14 位 faculty：3 位 3/3 PASS（Wiggins、Du、Ren——三人都同时 APAM 系 + DSI/CMM 关联 + lab 主页明确 ML 关键词）。Borcea 是 2/3（APAM + 数据驱动建模, 但非 ML 主线）。Bienstock 是 2/3（APAM 兼 IEOR + 优化非 ML 主线）。被删除：Sobel、Spiegelman、Tippett、Mandli（climate dynamics 非 ML）、Weinstein、Xuenan Li（PDE/分析非 ML）。
Bio
生物
MS in CS-Computational Biology Track（CS 系内, 30 学分）
Department of Systems Biology（仅 PhD, 通过 CMBS Integrated Program 申请）
MS in Biostatistics（Mailman SPH）
CS-CompBio + DSB
Bio Top 10
USNews
 强
CompBio Track 含 COMS W4762 ML for Functional Genomics约 60%
 强
CS ↔ Systems Biology 共聘
课程重合详情
CompBio Track + COMS ML 课重合
全部 重合
课号课程类型
COMS W4761Computational Genomics（Itsik Pe'er）CompBio Track 必修可选
COMS W4762Machine Learning for Functional GenomicsCompBio Track 必修可选
COMS W4771Machine Learning（Daniel Hsu）CompBio Track 必修可选
BIOL W4510Genomics of Gene Regulation（Harmen Bussemaker）CompBio elective
BINF G4015Computational Systems Biology（Dennis Vitkup）CompBio elective
BINF G4017Deep SequencingCompBio elective

Columbia 的"AI×Bio"通过多条强路径：(1) DBMI (Department of Biomedical Informatics) 在 CUIMC 校区, 自己 about 页面明文"machine learning over electronic health record data"是核心方向；(2) Department of Systems Biology（Mohammed AlQuraishi 蛋白质结构 ML, Raul Rabadan 数学基因组）；(3) Mailman School of Public Health Biostatistics 提供 MS in Biostatistics；(4) Stat 系 Paninski/Cunningham/Dumitrascu 做 computational biology + ML。

师资重合详情
Itsik Pe'er, Mohammed AlQuraishi 等
全部 joint primary
姓名主要方向关系
George HripcsakEHR, ML, NLP, causal inference, phenotyping; DBMI Chair; Vivian Beaumont Allen Professor; National Academy of MedicineDBMI Chair + DSI executive committee
Noémie ElhadadML, NLP, decision support, EHR; DBMI Chair; Health Analytics Center DirectorDBMI + DSI Health Analytics
Adler PerotteML, deep learning, causal inference, off-policy RL, EHRDBMI
Pierre EliasML, deep learning for cardiology and cardiac imagingDBMI + Medicine
Itsik Pe'erComputational genomics, statistical genetics, ML, immunogenomicsSystems Biology + DBMI + CS
Raul RabadanGenomics, ML, cancer, infectious diseases, tumor evolution; Director of Mathematical GenomicsSystems Biology + DBMI + Institute for Cancer Genetics
Liam PaninskiML for neural data analysis（cross-listed for Bio/Neuroscience students）Stat + Center for Theoretical Neuroscience + Grossman Center
Mohammed AlQuraishiProtein structure ML; AlphaFold-era PI（2021 加入）Systems Biology
Bianca DumitrascuComputational biology + ML（cross-listed）Stat dept

经三条标准筛查 DBMI training-faculty page 列表：6 位 3/3 PASS（Hripcsak、Elhadad、Perotte、Elias、Pe'er、Rabadan——都同时 DBMI dept + DBMI 自身就是 AI institute + lab 主页明确 ML/DL 关键词）。AlQuraishi 是 Systems Biology PI, 蛋白质结构 ML 顶级（2021 加入）。Paninski 和 Dumitrascu 已在 Stat 域记入。被删除：Gürsoy（DBMI 但 lab 是 genome privacy + 3D genome, 非 ML 主线，2/3）。
Chem
化学
MA in Chemistry（GSAS, 较少作为终端学位）
无 AI×Chem 专门 master
Chem MA 少, 无 AI 专项
Chem Top 15
估算
 弱
Chem MA 必修无 ML< 25%
 弱
无系统交叉
课程重合详情
Chem MA 必修与 AI 无重合
全部
课号课程类型

Columbia Chem MA 项目本身没有 AI 专门轨道（多数是 PhD 中途 MA）。但 Chem 系内有 CCCE (Columbia Center for Computational Electrochemistry)——明文使用"electronic structure theory, statistical mechanics, and machine learning approaches"——是 Reichman、Friesner 等 PI 主导的与 Schrodinger Inc. 合作的中心。Berkelbach 实验室也是 ML for quantum chemistry 方向。

师资重合详情
Chem 系教师无 ML 主线
全部 primary
姓名主要方向关系
David ReichmanTheoretical chemistry; CCCE PI（Columbia Center for Computational Electrochemistry, 与 Schrodinger 合作）, ML for electrochemistry/batteriesChemistry dept + CCCE PI
Tim BerkelbachQuantum chemistry + ML; "Data-Efficient Machine Learning Potentials" 2023; ML potentials for liquid waterChemistry dept + Flatiron Institute joint
Richard FriesnerQuantum chemistry, computational drug discovery; CCCE PI, schrodinger collaborationChemistry dept + CCCE

经三条标准筛查 Chem 系：CCCE 是 Chem 系下 ML 中心, 多位 PI 参与。Reichman 和 Friesner 是 CCCE 核心 PI（3/3）。Berkelbach 实验室明确 "Data-Efficient ML Potentials" 论文（3/3）。Chem MA program 本身仍是较弱 path（program design 没有 ML track）, 但研究层面有 ML × Chemistry 优秀 PI。
Phys
物理
MA in Physics（GSAS）
MA in Astronomy（Astrophysics）
无 AI×Physics 专门 master
MA Phys 存在但小
Phys Top 10
USNews
 中
部分 PhD 课程含 ML约 35%
 中
物理系内 ML 应用零散
课程重合详情
物理 PhD 课程中含 AI 元素
全部 重合
课号课程类型
PHYS G6090Computational PhysicsPhys grad elective
PHYS G6038Machine Learning for PhysicsPhys grad elective（按年开设）

Columbia 物理 master 路径有两条：(1) MA in Physics（GSAS）——传统物理；(2) MS in Applied Physics（APAM in SEAS）——含 ML × physics 方向（特别是 LEAP 和 Simoncelli 等 PI 的 physics-aware AI 研究）。Center for Theoretical Neuroscience 是 Stat + Phys + Neuroscience 跨界中心, Paninski、Cunningham 等是核心 PI, 物理学生可以通过这个中心做 ML for neural data。

师资重合详情
物理 × ML 教师有限
全部 joint primary
姓名主要方向关系
Michele SimoncelliPhysics-aware AI simulation methods for material properties from first principles; combines first-principles approach with ML; 2025 Charles Haenny Prize（2025 加入 APAM）APAM Applied Physics
Liam PaninskiStatistical ML for neural data; Center for Theoretical Neuroscience（Phys 系合作）+ Grossman Center co-directorStat + Center for Theoretical Neuroscience + Kavli Institute
John CunninghamStatistical ML for neuroscience; Center for Theoretical NeuroscienceStat + Center for Theoretical Neuroscience

Columbia 传统物理系（FAS Physics）大部分是 high-energy/condensed matter, ML 主线 PI 较少。但 APAM Applied Physics 有 ML × physics 新晋 PI（Simoncelli 2025 加入）, 且 Center for Theoretical Neuroscience 跨 Phys/Stat/Neuro 是 ML 应用入口。Paninski、Cunningham 已在 Stat 域记入, 此处反映他们对 Phys 学生的开放性。
Biz
商科
CBS · Columbia Business School MBA（含 Decision Sciences elective）
MS in Marketing Science（CBS）
MS in Applied Analytics（SPS, 兼职在线为主）
MS in Financial Economics（CBS）
CBS MBA + SPS Analytics
CBS MBA Top 10
USNews
 中
CBS MBA + SPS Analytics 含 ML 课约 45%
 中
CBS 与 IEOR 部分共聘
课程重合详情
CBS Decision Sciences elective + SPS Analytics 课
全部 重合 elective
课号课程类型
B8120Business Analytics（CBS, 必修）MBA 必修
B8122Decision ModelsMBA elective
IEOR E4571Personalization Theory（CBS-IEOR 联合）MS Marketing Science elective
SPS Applied Analytics 课程包—SPS Applied Analytics 核心
COMS W4995Applied DLCBS 学生需 override

Columbia 商科 ML 路径有两条强选项：(1) CBS Decision, Risk, and Operations (DRO) 自己 about 页面明文 "machine learning, statistics and econometrics" 是核心方法论；(2) IEOR FE 长期 QuantNet #1 quant program, E45XX 系列 ML 课程系列丰富。CBS Marketing Science 也含 ML（Ansari 等）。SPS Applied Analytics 是兼职在线为主, 不构成主流推荐。

师资重合详情
CBS × IEOR 联合 Personalization 等课
全部 joint primary
姓名主要方向关系
Daniel RussoStatistical ML × online decision making, RL, multi-armed bandits, recommender systems; Spotify ML consultantCBS DRO
Santiago BalseiroDynamic optimization, internet advertising, ML for revenue managementCBS DRO
Costis MaglarasStochastic modeling; CBS DeanCBS DRO
Asim AnsariMarketing science, ML in marketingCBS Marketing
Hardeep JoharIEOR FE Chair; Personalization Theory and Application；CBS-IEOR 联合教学IEOR + CBS Marketing Science elective
Jose BlanchetStochastic modeling, ML for OR/financeIEOR

CBS DRO 的 ML faculty 中, Russo 最为明显（Spotify ML consultant + lab page 明确 RL/ML）。Balseiro、Maglaras 是 dynamic optimization + ML 应用。IEOR Blanchet、Iyengar 等是 quant + ML 应用, 是 CBS-IEOR 联合教学的主力。

Columbia 的核心优势是 纽约市地理位置 + 工业界资源 + 多入口跨系结构（DSI / MA Stats Tracks / IEOR FE）。最佳路径：(1) DSI MS in DS（30 学分, 6 home dept, 灵活 elective track）；(2) MA Stats with ML/AI Track（2025 新设, 含 STAT GR5243）；(3) IEOR FE（quant + ML, 国际生认可度高）；(4) MS-CS-Computational Biology（生医背景的 CS 入口）。注意：化学 / 物理 master-level 路径很弱, 必须走 DSI MS in DS 或 MA Stats 而非本系 master。

来源：cs.columbia.edu · datascience.columbia.edu · stat.columbia.edu · ieor.columbia.edu · systemsbiology.columbia.edu · sps.columbia.edu
15

University of Massachusetts Amherst

麻省大学安默斯特分校 · Manning College of Information & Computer Sciences（2024 年改名）· 强化学习诞生地（首届 RLC 2024）· Sutton/Barto 博士培养所
USNews CS #21 (CS)
①

AI program 核心专业课 & Listed Faculty

UMass Amherst 的 AI 资源核心是 Manning College of Information & Computer Sciences（2024 年改名, 原 CICS）—— Andrew McCallum, Brendan O'Connor 等是 NLP/ML 学派代表。UMass 是强化学习的诞生地（Andrew Barto, Rich Sutton 早期工作于此）, 2024 年举办首届 Reinforcement Learning Conference (RLC)。研究生层面验证：MS in CS（30 学分, 4 core 必含 1 门 ML）、MS in Statistics（Math & Stat 系）、MS in Applied Math、MSBA（Isenberg）。

COMPSCI 389Introduction to Machine Learning（本科, 较新）
COMPSCI 489Reinforcement Learning（本科）
COMPSCI 583Big Data Algorithms
COMPSCI 585Introduction to NLP
COMPSCI 589Machine Learning（grad core）
COMPSCI 590RTopics in Reinforcement Learning
COMPSCI 670Computer Vision
COMPSCI 685Advanced NLP（grad, Brendan O'Connor）
COMPSCI 688Probabilistic Graphical Models
COMPSCI 689Machine Learning（PhD level）
COMPSCI 690RTopics in Probability and ML
COMPSCI 696DSTopics in Data Science
COMPSCI 412Quantum Information & Computation
STAT 535Statistical Computing
STAT 525Linear Regression Models

Listed Faculty（节选）：

Andrew McCallum Mohit Iyyer Hadi Pirsiavash Erik Learned-Miller Brendan O'Connor Hong Yu Daniel Sheldon Patrick Flaherty David Jensen Benjamin Marlin Justin Domke Madalina Fiterau Cameron Musco Marta Kwiatkowska Eugene Bagdasaryan Bruno Castro da Silva Philip Thomas

Andrew McCallum 是 NLP/IE 顶级学者（曾任 ACL President）。Philip Thomas, Bruno Castro da Silva 是 RL/AI Safety 方向, 直接传承 Sutton/Barto 学派。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

UMass MS in CS 4 core 必含 1 门 Machine Learning（官方明文）——这是少数把 ML 设为 master 必修核心区域的学校。COMPSCI 589 (grad ML) 对外系学生需 override, 但官方鼓励申请。

B · 学位计算

Stat / Math / Biostat 学生可以申请 Master's Certificate in Statistical & Computational Data Science（Stat + CICS 联合 cert, 与 MS 学位叠加, 30 学分）。这是非 CS master 拿 ML 课的正式制度化路径, 不需要单独申请额外学位。

来源：cics.umass.edu/academics/ms-computer-science-campus · cics.umass.edu/academics/academic-policies/graduate-programs-policies/ms-degree-requirements · umass.edu/mathematics-statistics/graduate-degree-statistics · ds.cs.umass.edu/education
③

与 AI 交叉的硕士项目（6 领域）

UMass × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MS in Statistics（Math & Stat 系内, 30 学分, in person 或 remote 都可）
Master's Certificate in Statistical & Computational Data Science（Stat + CICS 联合）
MS Stats + 联合 Cert
Stat Top 25
估算
 中
MS Stats elective 含 ML约 50%
 中
Stat ↔ CICS 联合
课程重合详情
MS Stats 与 COMPSCI ML 课重合（联合 Cert）
全部 重合 等价
课号课程类型
STAT 607Mathematical Statistics IMS 必修核心
STAT 608Mathematical Statistics IIMS 必修核心
STAT 525Linear Regression ModelsMS 核心
STAT 528Generalized Linear ModelsMS elective
STAT 535Statistical ComputingMS elective
STAT 597ML Topics（按年）MS elective
STAT 691PProject SeminarMS 项目选项
COMPSCI 589Machine Learning（grad）需 override；联合 Cert 必修

UMass MS Statistics 是 Math & Stat 系 30 学分项目, 提供同时 in person 和 remote 选项（这是相对独特的 flexibility）。Master's Certificate in Statistical & Computational Data Science 是 Stat + Manning CICS 联合 cert, 让 MS Stats 学生把 COMPSCI 589 等 ML 课纳入 elective。

师资重合详情
Stat × CICS 联合 cert 即跨系
全部 primary
姓名主要方向关系
Markos KatsoulakisGenerative Modeling, Scientific Machine Learning, Information Theory, UQ; teaches Math 690STF Mathematics of Generative AI; NeurIPS 2024, ICML 2023, JMLR 2022Math & Stat dept primary; Director Applied Math
Patrick Flahertylarge-scale genomic data, hierarchical Bayesian models, variational inference; lab page明文 machine learning, bioinformaticsMath & Stat dept (Stat side) primary
Lulu KangStatistical Learning, Bayesian Statistics, Approximate Inference, Application of Data Science; Stat Program DirectorMath & Stat dept (Stat) primary
Anna LiuNon-parametric statistics, mixed models, active learning, optimization, anomaly detection, single cell RNAMath & Stat dept (Stat) primary
Erin ConlonBayesian methods for data science, big data, bioinformatics, biostatistics; Stat MS Director (Charles River/Newton)Math & Stat dept (Stat) primary
Nathan WycoffDigital Twins, Bayesian Optimization, Transfer Learning; numerical optimization for ML, variational BayesMath & Stat dept (Stat) primary
Qian ZhaoSelective inference, density estimation, data science educationMath & Stat dept (Stat) primary

经三条标准筛查 UMass Stat 14 位 active faculty + 3 位 stat-adjacent (math 一侧)：7 位通过 AI keyword 过滤。Katsoulakis 全 3/3：Math & Stat dept primary + Applied Math & Computation Center 联通 CDS & Manning CICS + lab 页面明确 Generative ML/Scientific ML。Flaherty 全 3/3：Stat dept + lab 主页明文"machine learning, bioinformatics"。Wycoff、Kang、Liu、Conlon 都是 stat 主聘 + lab 主页明确 ML/data science 关键词。被删除：Staudenmayer、Kim、Gile、Griffin、Michael、Westling、Soto、Li、Wixson（lab 主页无明显 AI keyword）。
Math
数学
Applied Mathematics MS（Math & Stat 系内, 2 年 professional）
注：MS in Math 通常仅 PhD 学生在途中获得
Applied Math MS 是入口
Math Top 30
估算
 中
Applied Math MS 含 ML 课程约 40%
 中
Math ↔ CICS 选课
课程重合详情
Applied Math elective 中含 ML 的课
全部 重合 elective
课号课程类型
MATH 605Probability Theory IApplied Math 核心
MATH 645Differential Equations & Dynamical SystemsApplied Math 核心
MATH 651Numerical Analysis IApplied Math 核心
MATH 671Topics in Applied MathApplied Math elective
STAT 607Mathematical Stats I可替代 MATH 605
COMPSCI 589ML需 override 或 Concentration

UMass Applied Math MS 是 2 年 professional 项目, 由 Director Markos Katsoulakis 主管。Katsoulakis 自身研究方向是 Scientific Machine Learning + Generative Modeling, NeurIPS/ICML/JMLR 等顶会都有论文。Center for Applied Mathematics & Computation 主页明文 "data science and machine learning" 是核心研究方向之一。也参与 NSF-funded TRIPODS Institute（CS + Math/Stat + ECE）。

师资重合详情
Math × ML 教师有限
全部 primary
姓名主要方向关系
Markos KatsoulakisGenerative Modeling, Scientific Machine Learning; Director Applied Math; NeurIPS 2024 Score-based generative models, ICML 2023, JMLR 2022Math & Stat dept primary, Center for Applied Math & Computation Director
Yao LiMachine Learning of Dynamical systems; Deep learning using Graph Neural Network; Financial MathematicsMath & Stat dept primary
Luc Rey-BelletCo-author Katsoulakis ML/divergence papers (JMLR, NeurIPS, ICML 2023)Math & Stat dept primary
Paul DupuisProbability theory + ML applications via Katsoulakis collaborationMath & Stat dept primary
Weiqi ChuNetwork science; Data-driven modeling and inference; Model reduction and scientific computingMath & Stat dept primary (stat-adjacent)

UMass Math & Stat 系是统一系（Math + Stat）。Applied Math 一侧的 ML 主线 PI 集中在 Katsoulakis 研究组。Yao Li 是独立 ML × dynamical systems PI（Math 系页面明文 deep learning + GNN）。Rey-Bellet 和 Dupuis 是 Katsoulakis 研究合作者，共同发表 JMLR/NeurIPS。Chu 是数据驱动建模方向的 stat-adjacent。被删除：Hans Johnston (numerical 但非 ML), Nathaniel Whitaker (math education), Andrea Nahmod (analysis 非 ML)。
Bio
生物
MS in Biostatistics（公共卫生学院 SPH, 流行病学+生物统计）
UMass Amherst 没有专门的 Bioinformatics MS 项目
Biostat 在 SPH
Bio Top 50+
估算
 中
Biostat MS 含 stat ML约 35%
 中
SPH × CICS 偶有合作
课程重合详情
Biostat 课 + COMPSCI ML 重合
全部 重合 elective
课号课程类型
BIOSTATS 690STStatistical Learning（按年）Biostat MS elective
BIOSTATS 540Intro to BiostatisticsBiostat 核心
COMPSCI 589ML（grad）需 override

UMass MS in Biostatistics 是 SPH（School of Public Health and Health Sciences）的项目。SPH Biostat 自身页面明文 "Areas of expertise include Bayesian methods, biomarker discovery, causal inference, machine learning, observational studies, randomized trials, and time-to-event outcomes"。UMass 是 COVID-19 Forecast Hub 国家级中心（由 Reich 主持），是 ML × public health 的旗舰项目。注：UMass Amherst 没有独立 Bioinformatics MS。

师资重合详情
Biostat 教师有 ML 应用
全部 joint primary
姓名主要方向关系
Nicholas Reichforecasting, machine learning, time series, infectious disease modeling, cluster-randomized trials; Director COVID-19 Forecast Hub; Director Influenza Forecasting Center of ExcellenceSPH Biostatistics primary
Zhengqing Ouyangstatistical genomics and bioinformatics; data integration; network modeling; manifold learning; predictive learningSPH Biostatistics primary
Patrick Flahertylarge-scale genomic data, hierarchical Bayesian models, variational inference; ML, bioinformatics, statistics, genetics（cross-listed for Bio students）Math & Stat (Stat) primary, but lab works on biomedical data
Leontine AlkemaBayesian inference; statistical demography; global child, maternal, reproductive health; 2025 Highly Cited ResearcherSPH Biostatistics primary, Associate Chair

经三条标准筛查 SPH Biostat faculty：Reich 全 3/3（SPH Biostat dept + COVID Forecast Hub Director + lab 页面明文 forecasting + ML）。Ouyang 全 3/3（SPH Biostat + lab 明确 manifold learning + predictive learning + 学生 ML papers）。Flaherty 跨系（Stat 主聘但 lab 做 biomedical ML, 因此对 Bio 学生开放）。Alkema 是 Bayesian 主线（高度引用研究员）。被删除：其他 Biostat faculty 缺少 ML 关键词或主线非 ML。
Chem
化学
MS in Chemistry（少见为终端学位, 主要 PhD 中途获得）
无 AI×Chem 专门 master
Chem MS 罕见
Chem Top 50+
估算
 弱
Chem MS 必修无 ML< 25%
 弱
无系统交叉
课程重合详情
Chem MS 必修与 AI 几乎无重合
全部
课号课程类型

UMass Chem MS 项目本身较少（多为 PhD 中途）, 但 Chemistry 系有明确的 ML × chemistry 主线 PI。Zhou Lin 实验室主页明文 "machine learning aided high-throughput materials discovery"。Scott Auerbach 与 EPFL Ceriotti 合作的 AI for zeolite work（"sorting hat"）发表在 Digital Discovery，被 UMass News 报道。Chemistry research page 把 "Computational Chemistry" 列为 5 大研究方向之一。

师资重合详情
Chem 系无 ML 主线 faculty
全部 primary
姓名主要方向关系
Zhou LinQuantum chemistry; Artificial intelligence; Density functional theory; Carbon neutrality; Molecular spectroscopy; Sanibel Symposium Young Investigator Award (ML acceleration of DFT); ML-aided high-throughput materials discoveryChemistry dept primary
Scott AuerbachAI/ML for materials, "sorting hat" algorithm for zeolite synthesizability; Digital Discovery 2022 publishedChemistry dept primary

经三条标准筛查 UMass Chem 系：Lin 全 3/3（Chem dept + 实验室主页明确 AI + Sanibel ML award）。Auerbach 全 3/3（Chem dept + UMass News 专文报道 AI work + Digital Discovery 论文）。其他 Chem 教授（如 Jianhan Chen, Min Chen 做 computational biology, Vince Rotello 做 nanomaterial 合成等）以化学计算为主，ML 不是 lab 主线。
Phys
物理
MS in Physics（主要 PhD 中途获得）
UMass 物理系有 Quantum Computing 方向
COMPSCI 412 = Quantum Information & Computation（CICS 内）
Phys MS 罕见
Phys Top 30
估算
 中
物理无独立 AI 课, 但 CS 量子方向开放约 30%
 中
无系统交叉
课程重合详情
物理 PhD 课程 + 量子方向有 ML 应用
全部 重合
课号课程类型
COMPSCI 412Quantum Information & ComputationCICS 内, 物理学生可选

UMass Physics 系传统强项是粒子/凝聚态物理（Borexino, ATLAS, g-2 等大型实验）, 没有独立的 AI×Physics master 路径。Quantum Computing 方向（QIS Institute）与 Manning CICS 的 COMPSCI 412 关联, 但物理系本身的 ML 主线 PI 在公开 faculty page 上未明确呈现。物理学生若想学 AI, 需通过 COMPSCI 589 等 CICS 选课。

师资重合详情
物理 × ML 教师较少
全部
姓名主要方向关系

经查 umass.edu/physics/people/faculty 主页面：列出的 PI 如 Michael Ramsey-Musolf (theoretical particle/nuclear/cosmology), Andrea Thamm (BSM physics), Donald Candela (condensed matter) 等都没有 ML 作为研究主线（lab 页面无 AI 关键词）。基于"无官方依据则不列"原则, 此格留空。
Biz
商科
Isenberg School of Management MS in Business Analytics（30 学分）
MBA 含 Data Analytics concentration
MassMutual Data Center 与 Isenberg / 数学系都有合作
Isenberg MSBA
Isenberg MBA Top 50+
估算
 中
MSBA 含 ML 课约 40%
 中
Isenberg × CICS 部分合作
课程重合详情
MSBA 必修 + COMPSCI elective
全部 重合 elective
课号课程类型
SCH-MGMT 791PBusiness Analytics（MSBA 必修）MSBA 核心
OPIM-XXX 系列Predictive Analytics 等MSBA elective
COMPSCI 589ML（grad）需 override

UMass Isenberg MSBA 是 30 学分 STEM 项目（含 Amherst on-campus + Newton + online flexible 三个选项）。MSBA 必修课包括 Machine Learning for Analytics（明文）。Isenberg 还提供 Graduate Certificate in Artificial Intelligence for Business（4 课程 12 学分, 课程学分可转 MBA/MSBA）。MassMutual Data Center 是 Amherst 当地的产业合作纽带。

师资重合详情
Isenberg × CICS 教师有限
全部 primary
姓名主要方向关系
Yi QuAnalytical modeling, optimization, revenue management, cloud computing; teaches OIM 390STB Artificial Intelligence in Business; PhD LSE, MSc Data ScienceIsenberg OIM primary
Marta Stelmaszak RosaDigital data, responsible AI; Data Science MSc; teaches OIM 350 Business Analytics; AI in business researchIsenberg OIM (Information Systems)
Anna NagurneyOperations Research, supernetworks, optimization; INFORMS 2025 President's Award; Director Virtual Center for SupernetworksIsenberg OIM primary, Eugene M. Isenberg Chair
Traci J. HessInformation Systems; Senior Associate DeanIsenberg OIM primary, Berthiaume Endowed Professor

经三条标准筛查 Isenberg OIM faculty：Qu 全 3/3（OIM dept + AI in Business 课程 + DS 学位背景）。Stelmaszak Rosa 全 3/3（OIM IS + DS 学位 + AI 业务研究）。Nagurney 是 OR 主线（INFORMS 顶级奖, supernetworks 与 ML 接壤）。Hess 是 IS 主线 + AI 教育扩展领导。Isenberg 与 Manning CICS 的正式 joint appointment 较少, 但通过 MSBA 课程实现 ML 对接。

UMass Amherst 的核心优势是 NLP & RL 的学派传承 + 公立校学费友好。最佳路径：(1) MS in CS（30 学分, ML 必修, 强 NLP/RL 训练）；(2) MS Stats + Master's Cert in Statistical & Computational DS（Stat + CICS 联合）；(3) MS Applied Math（Director Markos Katsoulakis 研究 Scientific ML）。注意：UMass 没有独立 Bioinformatics MS, 化学 / 物理 master-level AI 路径较弱, 主要靠 CICS 选课桥接。

来源：cics.umass.edu · umass.edu/mathematics-statistics · isenberg.umass.edu · ds.cs.umass.edu
16

University of Pennsylvania

宾夕法尼亚大学 · CIS · Warren Center · Wharton Stat & DS · DATS（MSE-DS）· MSQF（2025 新设, 50 年来首个新 master）
USNews CS #10 (CS) · Top 5 (Stat) · Top 1-3 (Wharton MBA)
①

AI program 核心专业课 & Listed Faculty

Penn 的 AI 资源核心在 SEAS 工程学院的 CIS 系 + Wharton 的 Statistics and Data Science 系（2024 改名）+ Warren Center for Network and Data Sciences（Michael Kearns 创办）。研究生层面验证：MSE-CIS（5 个 concentration: AI/CV/Systems/Software/Theoretical）、MCIT（专为非 CS 本科背景设计）、MSE-DS（DATS）（10 cu, 4 个 specialization 包括 Biomedicine, Network Science, Public Policy, Digital Humanities）、MSE-DS Online、SCMP、MSQF（2025 新, Wharton）。

CIS 5190Applied Machine Learning
CIS 5200Machine Learning（grad core）
CIS 5210Artificial Intelligence
CIS 5220Deep Learning
CIS 5300Computational Linguistics（NLP）
CIS 5810Computer Vision
CIS 5450Big Data Analytics
CIS 5500Database & Information Systems
CIS 6250Theory of Machine Learning（Michael Kearns）
CIS 6800Advanced Topics in Machine Perception
ESE 5410Machine Learning for Data Science
ESE 5420Statistics for Data Science
ESE 5450Data Mining: Learning from Massive Datasets
ENM 5310Data-driven Modeling and Probabilistic Sci Computing
STAT 5710Modern Data Mining
STAT 5050Statistical Inference for DS
CIS 5230Ethical Algorithm Design（Kearns / Roth）

Listed Faculty（节选）：

Aaron Roth Michael Kearns Surbhi Goel Lyle Ungar Mark Yatskar Chris Callison-Burch Dan Roth Eric Eaton Dinesh Jayaraman Kostas Daniilidis Jianbo Shi Pratik Chaudhari Yoseph Barash Hamed Hassani

Michael Kearns 是 Penn 跨系师资典型（CIS primary + Stat & DS + OID/Wharton + Economics）。Aaron Roth 是 Heilmeier Award 获得者。Surbhi Goel 是 Schmidt AI2050 Early Career Fellow。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

Penn 的 master 入口非常清晰：MCIT（专为非 CS 本科生设计的 CIS master, 含完整 CS 基础）、MSE-DS（DATS）（10 cu, foundations 2 + core 3 + tech electives 5, 含 Biomedicine/Network Science/Digital Humanities/Public Policy 4 个 specialization）。MSE-DS Online同样存在。

B · 学位计算

Stat & DS 系不向一般申请人开放 master, 但Wharton MBA + MSQF + OIDD master + DATS 之间有 Open Penn 跨学院选课的灵活性。DATS Biomedicine specialization是非 CS 本科 + 生医背景的官方入口（IBI 主导）。

来源：cis.upenn.edu · dats.seas.upenn.edu/programofstudy · catalog.upenn.edu/graduate/programs/data-science-mse · statistics.wharton.upenn.edu · online.seas.upenn.edu/degrees/mse-ds-online · wharton.upenn.edu
③

与 AI 交叉的硕士项目（6 领域）

Penn × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
Wharton 的 Stat 系自 2024 年改名为 "Statistics and Data Science"
MA in Stat & DS（Bridge to Doctorate, 2 年, 仅向 PhD 准备）
Dual MA Stat & DS（仅向已在 Penn 读 PhD 的学生）
Stat 系无开放式 MS（仅 Bridge）
Stat Top 5
USNews
 中
仅 Bridge MA（限制申请人）约 40%
 中
Stat ↔ CIS 共聘明确（Kearns）
课程重合详情
Bridge MA 课程 + CIS ML 课
全部 重合 等价
课号课程类型
STAT 5050Statistical Inference for DSBridge 推荐
STAT 5710Modern Data MiningBridge 推荐
STAT 9999Master's Independent StudiesBridge MA 必修
CIS 5200Machine LearningBridge 学生可申请
CIS 5450Big Data AnalyticsBridge 学生可申请

Penn Stat & DS 系没有开放给一般申请人的 master degree——只有两个限定项目：(1) Bridge MA（2 年, 仅向欠代表性背景准备 PhD 的学生）, (2) Dual MA（仅向已在 Penn 读 PhD 的学生）。一般想读 Stat 方向 master 的非 Penn 学生应申请 SEAS 工程学院的 MSE-DS（DATS）。但 Wharton Stat & DS 是美国统计 ML 实力最强的几个系之一（Top 5 全美），其 ML faculty pool 对 PhD 申请人和跨系选课学生极有吸引力。

师资重合详情
Michael Kearns 等跨系 Stat+CIS+OID
全部 joint primary
姓名主要方向关系
Dylan SmallDept Chair, Universal Furniture Professor; causal inference, observational studies; Bridge MA Co-DirectorWharton Stat & DS primary
Weijie SuStatistical ML, LLM, Differential Privacy, High-Dim Stats, Deep Learning Theory; ICML 2024; Sloan; IMS FellowWharton Stat & DS primary
Tony CaiStatistical ML, high-dim stats, large-scale inference; Daniel H. Silberberg ProfessorWharton Stat & DS primary
Yuxin ChenStatistics, optimization, ML; high-dim inference; SIAM Imaging Best Paper 2024Wharton Stat & DS + ESE secondary
Edgar DobribanStatistical ML, high-dim asymptotics, randomization tests, distributed learning, UQ for ML; IMS Early Career Prize 2024Wharton Stat & DS + CIS secondary
Jiaoyang HuangRandom matrix theory, optimization of deep neural networks, posterior sampling; Math secondaryWharton Stat & DS + Math secondary
Linda ZhaoStatistical ML, data-driven decision-making, bandits, RL, post-selection inferenceWharton Stat & DS primary
Jason AltschulerOptimization, probability, ML, mathematics of data science, optimal transport; PRiML affiliateWharton Stat & DS + CIS + ESE secondary
Bhaswar B. BhattacharyaNonparametric stats, networks analysis, statistical learning; Sloan FellowWharton Stat & DS + Math secondary
Yuting WeiHigh-dim stats, RL theory; Stein Fellowship, Lehmann CitationWharton Stat & DS primary
Anderson Ye ZhangSpectral analysis, ranking, mixture models, mean field variational inferenceWharton Stat & DS + CIS secondary
Bingxin ZhaoHigh-dim stats, biomedical data science; ICSA Outstanding Young Researcher 2024Wharton Stat & DS primary
Nancy R. ZhangComputational genomics; Vice Dean Doctoral Programs; Bridge MA Co-DirectorWharton Stat & DS primary
Abraham WynerApplied probability, info theory, statistical learning, AdaBoost JMLR; Sports AnalyticsWharton Stat & DS primary
Shivani AgarwalML theory, ranking; Rachleff Family Associate ProfessorCIS primary + Stat & DS secondary
Michael KearnsML/AI, algorithmic game theory, fairness; Founding Director Warren Center; CIS 6250 Theory of MLCIS primary + Stat & DS + OID + Economics 4-system

经三条标准筛查 Wharton Stat & DS 25 位 standing faculty + 跨系共聘：通过 AI 关键词过滤 16 位。Su、Cai、Chen、Dobriban、Altschuler 全 3/3（Wharton Stat dept primary + 主页明文 ML/data science + 顶会论文 NeurIPS/ICML/JMLR）。Huang 是 Stat + Math 双系（"optimization of deep neural networks"）。Zhao（Linda）lab 主页明文 RL + bandits。Wyner JMLR AdaBoost paper。Kearns 是经典跨系 PI（CIS + Stat + OID + Economics 四聘）。被删除：Boix、Davis、Hooker、Jensen、Jin（Y）、Katsevich、Low、Ren、Tchetgen Tchetgen（lab 主线非 ML）。
Math
数学
MA in Mathematics（GSAS）
MSE in Scientific Computing（SEAS, SCMP）
Dr. Bruce I. Jacobs MSQF · Master of Science in Quantitative Finance（Wharton, 2025 启动, $60M Jacobs 捐赠, 1 年, 强调 ML/AI）
SCMP + 新 MSQF
Math Top 15
估算
 中
SCMP / MSQF 直接含 ML 核心约 55%
 中
Math ↔ CIS 部分共聘
课程重合详情
SCMP / MSQF 与 CIS ML 课重合
全部 重合 等价
课号课程类型
CIS 5150Linear Algebra and OptimizationSCMP & DATS 必修
MATH 5130Computational Linear AlgebraSCMP / DATS 替代选项
ENM 5310Data-driven Modeling and Probabilistic Scientific ComputingDATS 核心选项
CIS 5200Machine LearningSCMP/MSQF 可纳入
STAT 5710Modern Data MiningDATS 核心选项
Wharton MSQF 6 core 课程（含 ML/AI）—MSQF 必修

2025 年 Wharton 启动了50 年来首个新 master 学位 MSQF（Master of Science in Quantitative Finance）, 由 Bruce I. Jacobs $60M 捐赠资助。这是 Penn 数学/经济量化方向 → AI 应用的最直接桥梁。SCMP（Scientific Computing Master's Program）与 DATS 共享 4 门核心课, 对物理/工程背景非常友好。AMCS Graduate Group 是跨系（Math + Stat + CIS + SEAS + Bio + Med）的 master/PhD 项目, faculty 来自 10 多个系。

师资重合详情
Math × CIS 共聘有限
全部 joint primary
姓名主要方向关系
Yoichiro MoriCalabi-Simons Prof Math & Bio; AMCS Chair; PDE analysis, mathematical physiology, dynamical systemsMath + Bio primary, AMCS Chair
Robert StrainNonlinear PDE, Mathematical Physics; AMCS facultyMath primary
Bhaswar B. BhattacharyaNonparametric stats, statistical learning, networks analysis; Math secondaryStat primary + Math secondary
Jiaoyang HuangRandom matrix theory, optimization of deep neural networks; Math secondaryStat primary + Math secondary
Weijie SuML, LLM, deep learning theory, optimization; AMCS affiliatedStat primary + AMCS affiliated
Nat TraskScientific machine learning, geometric mechanics, multiphysics/multiscale, scientific computingMEAM primary, AMCS affiliated
Paris PerdikarisDeep learning; Foundation models for physical simulation; Physics-informed neural networks; Generative modelsMEAM primary, AMCS affiliated
Ravi RadhakrishnanAI enabled physics-based models for engineering health; BE ChairBE/ChemBio primary, AMCS affiliated

Math 域的 ML faculty 主要通过 AMCS Graduate Group 与 cross-listed/secondary appointment 实现。Mori（AMCS Chair）+ Strain 是 Math primary。Bhattacharya/Huang 是 Stat 主聘 + Math secondary。Su 是 Stat 主聘 + AMCS affiliated。Trask、Perdikaris、Radhakrishnan 是 SEAS 工程系（MEAM/BE）但都明确 AMCS faculty。Penn AMCS 招生 page 直接列出 "machine learning" 为 thesis area。
Bio
生物
MS in Biotechnology（CAMB, 跨学院）
Master of Bioinformatics（Perelman School of Medicine 提供）
DATS Biomedicine specialization（Institute for Biomedical Informatics, IBI 主导）
IBI 是 DATS 的 specialization
Med School Top 5
USNews
 强
DATS Biomedicine track 含 ML约 60%
 强
Med School + CIS 共聘明确
课程重合详情
DATS Biomedicine + IBI 课程与 ML 重合
全部 重合 elective
课号课程类型
CIS 5300Computational Linguistics可选 DATS Biomedicine
IBIM (Institute for Biomedical Informatics 课程包)—DATS Biomedicine specialization 核心
CIS 5200Machine LearningDATS Biomedicine 必修
CIS 5450Big Data AnalyticsDATS Biomedicine 必修

Penn 没有像 UMich DCMB 那样独立的 Bioinformatics master 项目, 但DATS（MSE-DS）的 Biomedicine specialization 是正式入口, 由 Institute for Biomedical Informatics（IBI）主导。DBEI（Department of Biostatistics, Epidemiology & Informatics）是 Penn Med 内非常强的统计 + ML 中心, 多位教授在 AMCS 也有 appointment。Penn 的Center for AI and Data Science for Integrated Diagnostics (CAID)（Camara 是核心 PI）是 Bio × ML 的旗舰中心。

师资重合详情
Med School × CIS faculty
全部 joint primary
姓名主要方向关系
Hongzhe Li (Lee)Statistical genetics & genomics, GWAS, high-dim regression, NGS analysis; Perelman ProfessorBiostat (DBEI) primary
Mingyao LiStatistical genomics, machine learning, pathology imagingBiostat (DBEI) primary
Qi LongStatistical and ML methods for big complex health data, omics, EHR; bias and fairness in AI for medicine; BayesianBiostat (DBEI) primary
Li ShenMedical image computing, biomedical informatics, ML, network science, imaging genomics, multi-omics; Alzheimer's diseaseBiostat (DBEI) primary
Yong ChenReal-world data, evidence synthesis, learning health systems; data privacyBiostat (DBEI) primary
Kai TanGenomics, image analysis, ML, network science, big data biomedicine; CDB + Genetics + PediatricsPediatrics + CDB + Genetics
Pablo Gonzalez CamaraComputational biology, genomics, applied geometry, topological data analysis; CAID centerGenetics + IBI + Center for AI in Diagnostics
Junhyong KimSingle cell biology, evolutionary biology, algebraic statistics, machine learningBiology primary + CIS
Konrad KordingComputational Neuroscience, ML for neuroscienceBioengineering + CIS + Neuroscience
Yoseph BarashComputational Biology, RNA splicing MLCIS primary + Genetics

经三条标准筛查 DBEI Biostat 与 SAS 生物相关 faculty：Hongzhe Li、Mingyao Li、Qi Long、Li Shen 全 3/3（DBEI primary + lab 主页明文 ML/genomics + 同时是 AMCS faculty）。Tan 是 Pediatrics primary 但 lab 主页明文 ML for genomics。Camara 是 Genetics + IBI + CAID 三聘（topological data analysis）。Kim、Kording、Barash 是经典 cross-system ML PI。
Chem
化学
MA in Chemistry（GSAS）
无独立 AI×Chem master 项目
Chem MA 罕见
Chem Top 15
估算
 弱
Chem MA 必修无 ML< 25%
 弱
无系统交叉
课程重合详情
Chem MA 必修与 AI 无重合
全部
课号课程类型

Penn Chem MA 是 GSAS 项目, 但Penn AI 自身的 ai.upenn.edu/ai-science 页面专门列出 8 位 ML × Chemistry/Physics PI, 形成正式的 "AI for Chemistry" cohort：Kozlowski, Andrea Liu (Phys), Mallouk, Petersson, Rappe, Trauner, Zahrt, Durian (Phys)。Penn 强调 "synergy between experimentation and ML, especially focusing on the acquisition of high-quality data through advanced high-throughput experimentation and automation, and the development of physics-informed ML"。

师资重合详情
Chem 系无 ML 主线
全部 primary
姓名主要方向关系
Andrew M. RappeTheoretical physical chemistry, computational materials design, ferroelectrics, photovoltaics, catalysis; Blanchard Prof of Chem; APS Fellow; lab integrates ML in materials discoveryChemistry primary + MSE
Marisa C. KozlowskiListed in Penn AI Chemistry ML cohort (high-throughput experiment + ML)Chemistry primary
Thomas E. MalloukMaterials chemistry; Penn AI Chemistry ML cohortChemistry primary
E. James PeterssonChemical biology, peptide engineering; Penn AI Chemistry ML cohortChemistry primary
Dirk TraunerChemical biology, photopharmacology; Penn AI Chemistry ML cohortChemistry primary
Andrew ZahrtHigh-throughput experimentation + ML for organic chemistryChemistry primary

经查证 Penn AI 自身页面 (ai.upenn.edu/ai-science) 明确列出 Chemistry × ML PI cohort。Rappe lab 主页明文 "computational materials design" + Google Scholar 引用大量 ML 应用论文。Penn AI 是 Provost-level 跨学院 AI initiative, 其网页是这些 PI 工作 ML 主线的官方背书。
Phys
物理
MA in Physics（GSAS）
SCMP（适合物理→Sci ML 的桥梁）
无独立 AI×Phys master
SCMP 是物理常见出口
Phys Top 15
估算
 中
SCMP 直接对接物理背景约 40%
 中
物理 → CIS via SCMP
课程重合详情
SCMP 课程含 AI 应用
全部 重合
课号课程类型
SCMP 核心 4 门（与 DATS 共享）—SCMP 必修
CIS 5200Machine LearningSCMP elective

Penn 物理系本身有明确的 ML 主线 PI。Andrea Liu (Hepburn Prof, NAS, APS Kadanoff Prize 2025) 是 ML × soft matter 的开创者之一, 与 Google DeepMind/OpenAI 多位 alumni 合作。Vijay Balasubramanian 是 Computational Neuroscience Initiative (CNI) Director, 研究横跨 perception 到 ML。Penn AI 自身页面把 Liu 和 Durian 列为 Chemistry/Physics ML cohort 成员。SCMP（Scientific Computing Master's Program, SEAS 工程学院）是物理 / 工程背景转 AI 的官方推荐桥梁, 与 DATS 共享 4 门核心课。

师资重合详情
物理 × ML 教师有限
全部 joint primary
姓名主要方向关系
Andrea J. LiuHepburn Professor of Physics; Soft & Living Matter; Director Center for Soft and Living Matter; APS Kadanoff Prize 2025; NAS member; ML × disordered systems & soft matterPhysics & Astronomy primary
Vijay BalasubramanianTheoretical and computational neuroscience; CNI Director; "perception and cognition to ML"; Physics primaryPhysics & Astronomy + CNI Director
Eleni KatiforiBiophysics, condensed matter; AMCS facultyPhysics & Astronomy primary
Douglas DurianSoft matter physics; Penn AI Chemistry/Physics ML cohortPhysics & Astronomy primary
Dani Smith BassettNetwork science, neuroimaging, ML; J. Peter Skirkanich Professor; cross-listed PhysicsBE + ESE + Physics affiliated

经三条标准筛查 Penn Physics & Astronomy faculty：Liu 全 3/3（Phys dept primary + lab 主页 ML × physics + Penn AI 页面列出）。Balasubramanian 全 3/3（Phys primary + CNI Director + 直接被 CNI 介绍为 "perception to ML"）。Bassett 是 Bioengineering primary 但 AMCS faculty 页面明文 "machine learning"。Durian 是 Penn AI Chem/Phys cohort 成员。
Biz
商科
Wharton MBA + 多个 specialty masters：
MSQF · Master of Science in Quantitative Finance（2025 新）
OIDD（Operations, Info & Decisions）方向
MBA + concentrations（Quant Finance, Marketing Analytics 等）
Wharton MBA + MSQF
Wharton MBA Top 1-3
USNews
 强
MSQF 新设, 强调 ML/AI约 65%
 强
Wharton OID ↔ CIS 多位共聘
课程重合详情
Wharton MBA 含 ML 课 + MSQF 系列
全部 重合 elective
课号课程类型
OIDD 6510Decision Models & UncertaintyWharton 核心
OIDD 6620Big Data, Big ResponsibilitiesWharton elective
STAT 5710Modern Data MiningWharton 学生可选
Wharton MSQF 6 core + 30 elective—MSQF 必修
CIS 5200Machine LearningWharton 学生需 override

Wharton MBA 是 USNews #1-3。2025 新设的 MSQF 是 50 年来 Wharton 第一个全新 master 学位, 由 David Musto 任 Faculty Director, 强调 ML/AI 在量化金融的应用。Wharton AI & Analytics Initiative（AIA）是 Wharton 的旗舰 AI 中心。OIDD 是商科 master 转 AI 的传统强方向, 与 CIS 有多位共聘。

师资重合详情
Hamsa Bastani 等 OID + CIS 共聘
全部 joint primary
姓名主要方向关系
Hamsa BastaniML algorithms & applications to healthcare, revenue management, social good; OID primary, Stat secondaryOID Wharton primary + Stat secondary
Eric T. BradlowBayesian computation, latent variable models, marketing analytics; Vice Dean of Analytics at WhartonMarketing primary + Stat & DS + Economics + Education
Pinar YildirimMarketing, advertising, social networks, social media, news media, privacyMarketing primary + Economics secondary
Michael KearnsML/AI, algorithmic game theory, fairness; Founding Director Warren Center; OID secondaryCIS primary + OID + Stat & DS + Economics 4-system
David MustoWharton Finance, MSQF Faculty DirectorFinance primary
Jesus Fernandez-VillaverdeDynamic equilibrium models, ML estimationEconomics primary
Rakesh VohraGame Theory, Mechanism Design; Co-Director Warren CenterEconomics + ESE

经三条标准筛查 Wharton 商学院 ML PI：Bastani 全 3/3（OID primary + AMCS faculty + lab 主页明文 "ML algorithms"）。Bradlow 是 K.P. Chao Prof + Vice Dean Analytics + 4 系联合（Marketing/Stat/Econ/Edu）。Yildirim 是 Marketing primary + AMCS faculty。Kearns 是 4 系跨聘的经典 PI。Warren Center for Network and Data Sciences（Kearns + Vohra 联合主任）是 Penn 校级数据科学中心。

Penn 的核心优势是 跨学院结构非常成熟（CIS + Wharton + Med + Sciences）+ MCIT 给非 CS 背景的明确入口。最佳路径：(1) 非 CS 本科 → MCIT（先打 CS 基础）→ 再攻 MSE-DS；(2) 已有 CS/数学背景 → MSE-DS（DATS, 10 cu）；(3) 数学/物理 → SCMP 或 MSQF（2025 新设）；(4) 商科 → Wharton MBA + Quant Finance major；(5) 生医 → DATS Biomedicine specialization。注意：Penn Stat & DS 系不接受一般 master 申请, 化学 / 物理 master-level AI 路径较弱。

来源：cis.upenn.edu · dats.seas.upenn.edu · statistics.wharton.upenn.edu · wharton.upenn.edu · catalog.upenn.edu
17

Northwestern University

西北大学 · McCormick School of Engineering · IEMS · Kellogg MBA Top 1-5 · MLDS（前 MSiA, 2024 改名）
USNews CS #22 (CS) · Top 1-5 (Kellogg MBA)
①

AI program 核心专业课 & Listed Faculty

Northwestern 的 AI 资源核心是 McCormick School of Engineering 的 CS Department + IEMS（Industrial Eng & Mgmt Sciences）。研究生层面验证：MS in CS、MLDS（Master of Science in Machine Learning and Data Science, 2024 由 MSiA 改名）、MS in Statistics & Data Science、Northwestern SPS 在线 MS in DS。Kellogg MBA 是 USNews 长期 Top 1-5。

COMP_SCI 326Data Science Pipeline
COMP_SCI 348Intro to Artificial Intelligence
COMP_SCI 349Machine Learning（grad core）
COMP_SCI 449Deep Learning（formerly 396/496）
COMP_SCI 469Machine Learning for Robotics
COMP_SCI 461Deep Learning for NLP
COMP_SCI 463Generative Deep Learning
COMP_SCI 337Intro NLP
MSAI 349Machine Learning（MSAI 版本）
MLDS 400Math for ML
MLDS 401Stats for DS
MLDS 420Machine Learning II
MLDS 421Data Mining
MLDS 422Deep Learning
MLDS 424Generative AI（新, Klabjan）
IEMS 401Applied Statistics
IEMS 402Stochastic Processes

Listed Faculty（节选）：

Doug Downey Bryan Pardo Larry Birnbaum Kris Hammond V.S. Subrahmanian Han Liu Jason Hartline Diego Klabjan Aggelos Katsaggelos Daniel Apley

Diego Klabjan 是 MLDS Director, 也是 Generative AI 课的主讲。Han Liu 是 Stat & ML 顶级（前 Princeton）。Aggelos Katsaggelos 是 ECE 的图像/视频 ML 老牌。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

MLDS 是 cohort-based, 限制 55-60 人 的全日制项目, 在 McCormick Engineering 下（不是 Kellogg）。MLDS 课程对外系学生开放需要 override + 等空位（"open to students outside of the MLDS program, assuming space after MLDS students are enrolled"）。MS-CS 学生可以选 COMP_SCI 349/449 等 ML 课作为标准 elective。

B · 学位计算

Kellogg MBA 与 MLDS 之间有官方合作（Kellogg 列为 MLDS 合作院系）。Stat / Math 学生可以将 COMP_SCI 349 申请作为 elective。MSAI 是 CS 系下的 1 年制 AI master, 与 MS-CS 互补。

来源：cs.northwestern.edu · mccormick.northwestern.edu/machine-learning-data-science · class-descriptions.northwestern.edu/MLDS · kellogg.northwestern.edu
③

与 AI 交叉的硕士项目（6 领域）

NU × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MS in Statistics & Data Science（Stat 系）
MLDS · Master of Science in Machine Learning and Data Science（McCormick IEMS, 15 个月, 55-60 人 cohort, 2024 由 MSiA 改名, Diego Klabjan 任 Director）
Northwestern SPS 在线 MS in DS（Analytics & Modeling, Analytics Management）
MLDS + Stat MS + SPS 在线
Stat Top 25
估算
 强
MLDS 是直接 ML/AI master约 70%
 强
MLDS 与 CS 合作明文
课程重合详情
MLDS 课程含 ML/AI/DL 必修
全部 重合
课号课程类型
MLDS 400Math for MLMLDS 必修核心
MLDS 401Stats for DSMLDS 必修核心
MLDS 410Generating Business Value with AnalyticsMLDS 必修
MLDS 420Machine Learning IIMLDS 必修
MLDS 421Data MiningMLDS 必修
MLDS 422Deep LearningMLDS 必修
MLDS 424Generative AI（Diego Klabjan）MLDS 选修, 新设
MLDS 499Industry CapstoneMLDS 必修

Northwestern 2023-2024 把原 MSiA（Master of Science in Analytics）改名为 MLDS（Master of Science in Machine Learning and Data Science）, 由 Director Diego Klabjan 主推。程序在 McCormick Engineering 的 IEMS 下而非 Kellogg 商学院——这是常被混淆的关键。MLDS 限制 55-60 人 cohort, 全日制 15 个月。同时 Stat & DS dept 也提供 MS in Statistics and Data Science（21 位 faculty）。

师资重合详情
MLDS faculty + CS 共聘明确
全部 joint primary
姓名主要方向关系
Han LiuStatistical ML, deep learning, nonparametric structure learning, representation learning; PhD ML and Statistics from CMU; Orrington Lunt Professor of CS and StatsStat & DS dept primary + CS joint
Kaize DingData mining, ML, large foundation models, reliable and efficient AI for autonomous decision-making, knowledge-guided AI; AAAI New Faculty HighlightsStat & DS dept primary
Matey NeykovStatistics and machine learning; high-dim nonparametric estimation, variable selection, inference, graph property testing; Director of Graduate StudiesStat & DS dept primary
Bradly C StadieReinforcement learning, deep learningStat & DS dept primary
Wenxin JiangMathematical statistics, biostatistics, data mining, Bayesian statistics, econometricsStat & DS dept primary
Miklos Z RaczNetwork analysis, probabilistic graphical modelsStat & DS dept primary + CS joint
Diego KlabjanMLDS Founding Director; ML, deep learning, RL, generative AI; finance, transportation, sport, bioinformaticsIEMS primary, MLDS Director
Daniel ApleyStatistics, IEMSIEMS primary

经三条标准筛查 NU Stat & DS 系 15 位 standing faculty + IEMS：5 位通过 AI 关键词匹配。Han Liu 全 3/3：Stat & DS dept primary + CS joint + lab page 明文 "statistical ML, deep learning"。Ding 全 3/3：Stat dept primary + 主页明文 "ML, large foundation models" + AAAI 奖项。Neykov, Stadie, Klabjan 都是 ML 主线。被删除：Andrews (time series), Hedges (meta-analysis), Hongmei Jiang (bioinformatics), Severini (math stats), Tipton (causal inference), Wang (computational bio), Zabell (history)。
Math
数学
MS in Mathematics（数学系）
MS in Applied Math（数学系）
MS in Industrial Engineering with Analytics（IEMS）
IEMS Analytics 强
Math Top 25
估算
 中
IEMS 与 MLDS 共享课程约 50%
 中
IEMS ↔ CS 部分共聘
课程重合详情
IEMS / Math 课与 CS AI 课重合
全部 重合 elective 独有
课号课程类型
IEMS 401Applied StatisticsIEMS 核心
IEMS 402Stochastic ProcessesIEMS 核心
IEMS 469Dynamic ProgrammingIEMS elective
IEMS 490Special Topics（按年含 ML）IEMS elective
COMP_SCI 349Machine Learning（grad 版）需 override
MATH 450Mathematical Modeling纯数学

Northwestern McCormick ESAM（Engineering Sciences and Applied Math）系明确把 "Machine Learning and AI for Science" 列为 5 大研究方向之一。ESAM MS 是 1.5-2 年项目（适合 PhD 准备 + 工业入口）。IEMS 系排名 Top 5（程序运营层面）, MLDS 直接挂在 IEMS 下。数学系（Weinberg）MS 入口规模较小, 但 IEMS / 数学系学生可以选 COMP_SCI 349 作为 elective。ESAM 与 NICO（Northwestern Institute on Complex Systems）紧密结合。

师资重合详情
IEMS 教师含 ML 应用
全部 joint primary
姓名主要方向关系
Madhav ManiData-driven and AI approaches in mathematical biology, deep learning, single-cell omics; Simons Investigator (MMLS)ESAM primary, NICO Quant Bio Group Leader
Niall ManganData-driven and ML methods for dynamical systems, sparse identification, model discoveryESAM primary
Daniel LecoanetComputational fluid dynamics, ML for scientific simulationsESAM primary
Luís AmaralComplex systems, network analysis, ML, biomedical informatics; Erastus Otis Haven ProfessorESAM primary, joint Physics/Medicine
Daniel AbramsComplex systems modeling; NICO Co-DirectorESAM primary
Diego KlabjanMLDS Director, optimization & MLIEMS primary, ESAM affiliated
Hermann RieckeESAM Chair, computational neuroscienceESAM primary

经三条标准筛查 ESAM 12 位 core faculty + 共聘：6 位通过 AI 关键词匹配。Mani 全 3/3：ESAM primary + lab 主页明文 "data-driven and AI approaches" + Simons Investigator。Mangan 是 dynamical systems + ML 方向（Brunton/Kutz 学派）。Amaral 是复杂网络 + ML 跨学科领袖。Lecoanet 用 ML 加速科学模拟。被删除：Bayliss, Chopp, Kath, Miksis, Vlahovska, Volpert（lab 主线非 ML, 主要是 fluid dynamics, PDE, applied analysis）。
Bio
生物
NUIN Neuroscience（PhD 主导）
MS in Biotechnology（McCormick）
无独立 Bioinformatics MS
无 Bioinformatics MS
Bio Top 25
估算
 中
间接通过 CS / MLDS 选课约 40%
 中
CS × Bio 部分共聘
课程重合详情
CS NLP / ML 课用于 Bio 应用
全部 重合
课号课程类型
COMP_SCI 349Machine Learning（grad）需 override
COMP_SCI 396/496DL Special Topics可应用于 Bio

Northwestern Feinberg School of Medicine 有Institute for Artificial Intelligence in Medicine (I.AIM), 由 Abel Kho 任 Founding Director, Yuan Luo 任 Chief AI Officer。I.AIM 主管MS in Biomedical Informatics（含 Health Informatics 和 Bioinformatics 两个 concentration）。Department of Preventive Medicine 的 Biostatistics & Informatics 部门提供 MS in Biostatistics（其表述 "Bayesian methods, bioinformatics, causal inference, computational biology, statistical genetics"）。这是 Northwestern Bio × ML 的旗舰入口。

师资重合详情
CS × Bio faculty 有限
全部 joint primary
姓名主要方向关系
Yuan LuoChief AI Officer at NUCATS & I.AIM; PhD MIT EECS; Nature Medicine, JAMA papers; AAAI/KDD/CVPR/ACL/AMIAPreventive Medicine (Biostat & Informatics) primary, I.AIM Chief AI Officer
Abel KhoFounding Director Institute for AI in Medicine (I.AIM); biomedical informatics, learning health systems; FACMIMedicine + Preventive Medicine primary
Faraz AhmadHeart failure cardiologist + AI + biomedical informatics; Associate Director Bluhm Cardiovascular Institute Center for AICardiology + Preventive Medicine primary
David LiebovitzInternal medicine + clinical informatics + engineering training; biomedical informaticsMedicine + Preventive Medicine primary
Theresa WalunasDirector MS in Biomedical Informatics programPreventive Medicine primary, I.AIM affiliated
Madhav ManiML for single-cell omics, organismal development; ESAM + Adjunct Molecular BiosciencesESAM primary + Molecular Biosciences adjunct

经三条标准筛查 Feinberg I.AIM 关键 PI：Yuan Luo 全 3/3（Preventive Medicine primary + I.AIM Chief AI Officer + Nature Medicine + 顶会论文）。Kho 全 3/3（I.AIM Director + AMIA Fellow）。Ahmad 是 Cardiology primary + Center for AI Associate Director。Mani 是 ESAM primary 但 lab 也做 single-cell 生物 ML（适合 Bio 学生跨域）。
Chem
化学
MS in Chemistry（少见为终端学位）
无 AI×Chem 专门 master
Chem MS 罕见
Chem Top 10
估算
 弱
Chem MS 无 ML< 25%
 弱
无系统交叉
课程重合详情
—
全部
课号课程类型

Northwestern Chem MS 项目本身较少（多为 PhD 中途）, 但 Chemistry 系有明确的 ML × chemistry 主线 PI。Chad Mirkin 是 NU 的 King Faisal Prize 获得者, 公开被 NU News 描述为 "pioneer in AI-based materials discovery"（高通量合成 + ML + Megalibrary 结合）, 论文发表在 Science Advances。CHiMaD（Center for Hierarchical Materials Design, NIST 资助）是 NU + UChicago + Argonne 合作的材料 ML 中心。

师资重合详情
—
全部 joint primary
姓名主要方向关系
Chad MirkinAI-based materials discovery, ML-accelerated synthesis of polyelemental heterostructures (Science Advances 2021); George B. Rathmann Professor; Director International Institute for NanotechnologyChemistry primary + ChBE + BME + MSE + Medicine
George SchatzComputational chemistry, theoretical molecular spectroscopy, ML for materialsChemistry primary
Chris WolvertonComputational materials, ML for materials genome; CHiMaD; MSE primary, by courtesy in ESAM/Phys/ChemMSE primary + ESAM/Phys/Chem

经三条标准筛查 NU Chem 系 + MSE 跨系：Mirkin 全 3/3（Chem primary + 3 篇 NU News AI for materials 报道 + Science Advances 2021 ML paper）。Schatz 是计算化学领袖（与 ML 应用接壤）。Wolverton 是 MSE primary + by courtesy 在 ESAM/Phys/Chem, NU AI institutes page 列出他领导 Materials Informatics 方向。
Phys
物理
MS in Physics（PhD 中途获得）
MS in Applied Physics
无独立 AI×Phys master
Phys MS 罕见
Phys Top 25
估算
 中
通过 CS 选课约 30%
 中
部分 CS × Phys 合作
课程重合详情
—
全部 重合
课号课程类型
COMP_SCI 349Machine Learning需 override

Northwestern CIERA（Center for Interdisciplinary Exploration and Research in Astrophysics）是 ML × Astrophysics 旗舰中心, Director Vicky Kalogera 同时是 NSF-Simons SkAI Institute Director（与 UChicago 合作）。CIERA 的"AI at CIERA" 页面明确列出多位 ML 主线 PI, 涵盖 cosmological simulation emulator, gravitational wave classification, 时间域天文学 ML 等方向。Northwestern Physics MS 主要为 PhD 中途取得, 但 CIERA Integrated Data Science Certificate 是物理学生明文接入 ML 的入口。

师资重合详情
—
全部 joint primary
姓名主要方向关系
Vicky KalogeraCIERA Director, NSF-Simons SkAI Institute Director; ML for binary star simulation (POSYDON), ML to characterize gravitational wave noise; Daniel I. Linzer Distinguished University ProfessorPhysics & Astronomy primary, CIERA + SkAI Director
Claude-André Faucher-GiguèreCosmological simulations + ML emulators for galaxy surveys (Rubin/Roman) and CMB experimentsPhysics & Astronomy primary, CIERA
Aggelos KatsaggelosImage and video processing, ML, data science; Joseph Cummings Professor of ECE + RadiologyECE primary + Radiology, CIERA Deputy Director of Computation
Adam MillerWide-field time-domain astronomical surveys + ML classification; LSSTC Data Science Fellowship Program DirectorPhysics & Astronomy primary, CIERA
Emma AlexanderBio-inspired physics-based computer visionPhysics & Astronomy primary
Madhav ManiML for biological systems; ESAM + Theoretical & Computational Soft MatterESAM primary, Physics affiliated

经三条标准筛查 NU CIERA 与 Physics 系 PI：Kalogera 全 3/3（Physics primary + CIERA Director + SkAI Director + ML 主线明确）。Faucher-Giguère 全 3/3（Physics + CIERA + ML emulator 工作）。Katsaggelos 是 ECE primary 但 CIERA 的 Deputy Director of Computation 头衔与 Phys 高度耦合。Adam Miller, Emma Alexander 是直接的 ML × physics PI。
Biz
商科
Kellogg MBA（多个 specialization 含 Operations & Analytics）
MMM Program（MBA + MS Design Innovation 联合）
MLDS（虽然在 McCormick, 但 Kellogg 是合作院系）
Kellogg MBA + MLDS
Kellogg MBA Top 1-5
USNews
 强
MLDS 与 Kellogg 课程互通约 60%
 强
MLDS 项目与 Kellogg 联合明文
课程重合详情
MLDS 课程 + Kellogg specialty 课
全部 重合 独有
课号课程类型
OPNS 430Operations Management（Kellogg）MBA 核心
DECS 430Statistical Decision AnalysisMBA 核心
MORS 472Bargaining and NegotiationMBA elective
MLDS 410Generating Business Value with AnalyticsMBA 学生可申请

Kellogg School of Management 是 USNews MBA Top 1-5。MLDS（前 MSiA, 2024 改名）虽然在 McCormick Engineering 的 IEMS 系下, 但 Kellogg 是其官方合作院系（与 CS 系并列, 见 mccormick.northwestern.edu/machine-learning-data-science/overview）, 这意味着 MLDS 学生可以选 Kellogg 课作为 cross-school elective。MLDS 是 Northwestern 商科 + ML 的主入口, 全日制 15 个月 cohort 制 55-60 人。Malthouse 在 Medill (传播学院) 的 Spiegel Research Center 把 ML 应用到 marketing。

师资重合详情
MLDS faculty + Kellogg 师资
全部 joint primary
姓名主要方向关系
Diego KlabjanMLDS Director; ML, deep learning, RL, generative AI; finance, transportation, sport, bioinformatics applications; led projects with Intel, AbbVie, FedExIEMS primary, MLDS Director
Edward MalthouseErastus Otis Haven Professor (Medill); Research Director Spiegel Research Center; ML for marketing/customer analyticsMedill primary, MLDS faculty
Sanjay MehrotraEmma Ann Reynolds Professor of IEMS; Director Center for Engineering and Health, Co-Director Project MinervaIEMS primary, MLDS faculty
Eric AndersonMarketing, Kellogg; data-driven marketingKellogg primary
Florian ZettelmeyerMarketing, Kellogg; data analytics for marketingKellogg primary
Ashish PujariAdjunct Professor; teaches MLDS Deep LearningMLDS faculty

经三条标准筛查 NU MLDS faculty 与 Kellogg：Klabjan 全 3/3（IEMS primary + MLDS Director + lab 主页明文 ML/DL/RL/generative AI）。Malthouse 是 Medill primary + MLDS 跨学院师资。Mehrotra 是 IEMS Center for Engineering and Health 总监。Anderson 和 Zettelmeyer 是 Kellogg Marketing 数据派代表。

Northwestern 的核心优势是 商工融合（McCormick + Kellogg 联合传统）+ MLDS cohort 制 + Kellogg MBA 顶级声誉。最佳路径：(1) 数据科学 / 商科 → MLDS（cohort 限 55-60, 全日制 15 个月）；(2) AI 工程 → MS-CS 或 MSAI；(3) 商科 → Kellogg MBA + Operations specialization；(4) 在职 → Northwestern SPS 在线 MS in DS。注意：Northwestern Bio / Phys / Chem master-level AI 路径较弱, 主要靠 CS 选课。

来源：cs.northwestern.edu · mccormick.northwestern.edu · kellogg.northwestern.edu · sps.northwestern.edu
18

University of Southern California

南加州大学 · Viterbi 工程学院 · Thomas Lord Department of Computer Science（2021 改名）· ISI（Information Sciences Institute, 知名独立研究所）· DEN@Viterbi 在线
USNews CS #20 (CS) · Top 25 (Marshall MBA)
①

AI program 核心专业课 & Listed Faculty

USC 的 AI 资源核心在 Viterbi 工程学院的 Thomas Lord Department of Computer Science（2021 年改名以纪念捐赠者）+ ISI（Information Sciences Institute）（位于 Marina del Rey 的独立研究所）。研究生层面验证：MS in CS（28 units）、MS in CS-Artificial Intelligence（32 units, 美国首批同类项目之一）、MS in CS-Scientists & Engineers track（专为非 CS 背景设计, 必先修 CSCI 455x）、MS in Applied Data Science（DSI）、Marshall MSBA 等。

CSCI 544Applied NLP
CSCI 545Robotics
CSCI 555Advanced Operating Systems
CSCI 559ML I: Supervised Methods
CSCI 561Foundations of AI（必修, 4 units）
CSCI 565Software Architectures
CSCI 566Deep Learning & Applications
CSCI 567Machine Learning（grad core）
CSCI 568Requirements Engineering
CSCI 570Algorithms（必修, 4 units）
CSCI 571Web Technologies
CSCI 572Information Retrieval
CSCI 575Statistical Methods
CSCI 662Advanced NLP（Kevin Knight）
DSCI 552Machine Learning for DS
DSCI 558Building Knowledge Graphs
DSCI 549Foundations of Data Management

Listed Faculty（节选）：

Yan Liu Aram Galstyan Bistra Dilkina Salman Avestimehr Lin Chen Fei Sha Jay Pujara Aiichiro Nakano Gaurav Sukhatme Antonio Ortega Jesse Thomason Erdem Biyik Stefanos Nikolaidis Stephen Tu

Yan Liu 2024 年 11 月一小时内同时获 AAAI 和 IEEE 顶级 ML 奖项。Aram Galstyan 是 ISI Research Director。Kevin Knight（NLP 经典）在 ISI。Antonio Ortega（信号处理 + ML）。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

USC 独特设计：MS-CS Scientists & Engineers track 专为非 CS 本科背景设计——必须先修 CSCI 455x（first sem）, 不能在第一学期选 CSCI 561/566/567。这是非 CS 转 CS-AI 的官方入口。MS-CS-AI（32 units）是美国首批 AI 专门 master 之一。

B · 学位计算

MS-ADS（Viterbi DSI）是商科+技术中间路径, 课程以 DSCI 前缀。DEN@Viterbi 提供大部分 MS 项目的在线版本——这是常春藤外最完整的工程在线 master 平台之一, 全美 Top 5 在线 CS。

来源：cs.usc.edu/academic-programs/masters · viterbigradadmission.usc.edu · catalogue.usc.edu · dsi.usc.edu
③

与 AI 交叉的硕士项目（6 领域）

USC × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MS in Applied Data Science（Viterbi DSI）
MS in Statistics（Math 系）
USC 没有独立 Stat 系（Stat 在 Math 系内）
MS-ADS 是主要 DS master
Stat 不独立
—
 中
MS-ADS 含 ML 课约 50%
 中
Math/Stat ↔ CS 部分共聘
课程重合详情
MS-ADS 与 CS AI 课重合
全部 重合 等价
课号课程类型
DSCI 552Machine Learning for Data ScienceMS-ADS 必修核心
DSCI 558Building Knowledge GraphsMS-ADS elective
DSCI 549Foundations of Data ManagementMS-ADS 核心
CSCI 567Machine Learning（grad core）需 override
CSCI 566Deep Learning & Applications需 override
STAT 510Statistical LearningMS Stat elective

USC 没有独立的 Statistics 系——Stat 在 Dornsife 数学系内。MS in Applied Data Science（MS-ADS）是 Viterbi 的旗舰数据科学 master, 由 DSI（Data Science Initiative）运行, 课程以 DSCI 前缀；MS in Statistics（Minsker 是 Director）是 Math 系下另一个直接选项；MS in Mathematical Data Science（MDS） 由 Lauda（数学系系主任）/Chen/Minsker 等推动 2025 年新设。Math Stat group 总共明文列出 18+ 位 PI, 含强 ML 派系。

师资重合详情
Stat × CS faculty 有限
全部 joint primary
姓名主要方向关系
Jinchi LvStatistics, machine learning, data science, AI; Kenneth King Stonier Chair (Marshall) + Professor of Mathematics; Royal Statistical Society Guy MedalMath primary + Marshall DSO joint, MASCLE faculty
Stanislav MinskerStatistical learning theory, high-dim statistics, NSF CAREER for ML algorithms; MS Statistics Program DirectorMath primary, MASCLE faculty
Xiaohui ChenHigh-dim statistics, machine learning, optimal transport; MDS Program Co-directorMath primary
Yizhe ZhuProbability, neural networks, random matrices, community detection, differential privacyMath primary
Dmitrii OstrovskiiMachine learning, statistics, optimization, signal processingMath primary
Yingying FanStatistical machine learning; PhD Statistics from Princeton; Tibshirani studentMarshall DSO primary, MASCLE faculty
Steven HeilmanProbability, theoretical CS, ML, RL, LLMMath primary
Larry GoldsteinHigh-dim statistics, concentration inequalities, Stein methodMath primary

经三条标准筛查 USC Math 系 Stat group 18+ 位 faculty + Marshall DSO joint：8 位通过 AI 关键词匹配。Lv 全 3/3：Math + Marshall joint + Royal Statistical Society Guy Medal + Math 系 Stat page 明文 "ML, AI"。Minsker NSF CAREER for "ML algorithms"。Xiaohui Chen 是 MDS Co-Director, GenAI grant。Fan 是 Tibshirani 学生, MASCLE faculty。被删除：Alexander, Arratia, Fulman, Goldstein 等 (probability 主线但非 ML), Ma, Mikulevicius (financial mathematics)。
Math
数学
MS in Applied Mathematics（Math 系）
MS in Mathematical Finance（Math + Marshall 联合）
Master of Engineering（Viterbi 多个工程方向, 含 CS 元素）
数学 → 工程 master 可桥接
Math Top 30
估算
 中
MS Math 选课灵活约 40%
 中
Math ↔ CS 部分共聘
课程重合详情
MS Math elective 含 CS AI 课
全部 重合 elective
课号课程类型
MATH 502Numerical Analysis IMS Math 核心
MATH 555Partial Differential EquationsMS Math 核心
MATH 565Optimization TheoryMS Math elective
CSCI 567Machine LearningMath 学生可选
DSCI 552ML for DS可选

USC 数学系（Dornsife）同时承担 Stat 系职能, 数学系内部明确把 "Mathematical Data Science" 作为 MS 主线之一。MS in Mathematical Data Science（MDS, 2025 新设）由数学系系主任 Lauda 和 Co-Directors Chen + Minsker 推动。Math 系 PhD 学生培养路径含 Stat 方向。Math 系 + Marshall + ISI 之间通过 MASCLE Center 形成跨系 ML 网络。

师资重合详情
Math × CS 共聘有限
全部 joint primary
姓名主要方向关系
Xiaohui ChenHigh-dim statistics, machine learning and optimal transport; MDS Program Co-director; Quantum Empowered Generative AI grantMath primary, MDS Co-Director
Stanislav MinskerStatistical learning theory, high-dim statistics, NSF CAREER ML algorithmsMath primary
James AlcalaOptimization, neural collapse phenomena, ML; thesis on saddle point/minimax problemsMath primary
Steven HeilmanProbability, ML, RL, large language modelsMath primary
Yizhe ZhuProbability, neural networks, random matrices, differential privacyMath primary
Jinchi LvMath + Marshall joint, ML and statisticsMath primary + Marshall DSO joint
Lin ChenStatistics and MLMath primary

经三条标准筛查 USC Math 系 stat / probability / applied math group：7 位通过 AI 关键词匹配。Xiaohui Chen 全 3/3（Math primary + MDS Co-Director + GenAI grant）。Minsker NSF CAREER for ML。Alcala 工作 saddle-point 优化用于 ML, neural collapse。Heilman lab 主页明文 "ML, RL, LLM"。Zhu 是网络分析和 deep learning 主线。被删除：Alexander, Fulman, Goldstein 等 probability 老牌但非 ML 主线。
Bio
生物
MS in Biomedical Engineering（Viterbi BME 系）
MS in Translational Biotechnology
无独立 Bioinformatics MS, 但 Quantitative Biology 方向通过 BME / DSCI 选课
无 Bioinformatics MS
BME Top 15
估算
 中
BME 课程含 ML 应用约 40%
 中
BME × CS 部分合作
课程重合详情
BME 课 + CS AI 课重合
全部 重合 elective
课号课程类型
BME 525Biomedical ImagingBME 核心
BME 526Engineering Approaches in Brain MappingBME elective
CSCI 567Machine LearningBME 学生可选

USC Keck School of Medicine 的 Population & Public Health Sciences 部门是 USC Bio × ML 的旗舰入口。Keck Biostatistics Division 在 2022 年获 $10.5M NCI P01 grant 专门用于 ML × cancer research（"rarely awarded for solely statistical methodology research"）, Co-PI 是 Gauderman 和 Siegmund。USC ISI 的 AI4Health Center（Director: Michael Pazzani）和 Keck 战略合作, 包括 Keck School, Ostrow Dental, Mann Pharmacy, Norris Cancer 等。MS in Biostatistics + MS in Public Health Data Science 是直接 entry-level master。

师资重合详情
BME × CS 共聘有限
全部 joint primary
姓名主要方向关系
David ContiML for cancer research; Kenneth T. Norris Jr. Chair in Cancer Prevention; Associate Director for Data Science Integration at Keck; Keck news quoted: "machine learning is a primary enabling AI technique"Population & Public Health Sciences primary
William GaudermanBiostatistics, ML for genetics; Division Chief; Co-led $10.5M NCI P01 ML grantPopulation & Public Health Sciences primary, Biostat Division Chief
Kimberly SiegmundBiostatistician, cancer modeling, epigenetic data analysis; Co-led $10.5M NCI P01 ML grantPopulation & Public Health Sciences primary, Associate Chief of Education
Sandrah EckelBiostatistics + generative AI for biomedical research; Department Pedagogical Excellence AwardPopulation & Public Health Sciences primary
Wael AbdAlmageedAI for medical imaging, congenital adrenal hyperplasia diagnosis via facial recognition; ISI Research DirectorISI primary, Keck collaborator (AI4Health)
Carl KesselmanWilliam H. Keck Professor of Engineering; ISI Fellow + Director of Informatics Systems Research; FaceBase 3 data hubISI Fellow + Industrial & Systems Engineering + Population & Public Health
Michael PazzaniAI4Health Center Director; ML for healthcare and misinformation; ISI Principal ScientistISI primary, AI4Health Director
Arthur TogaDirector USC Stevens Neuroimaging and Informatics Institute; ML in neuroimagingUSC Stevens Institute primary

经三条标准筛查 USC Keck Biostat + ISI AI4Health：8 位通过 AI 关键词匹配。Conti 全 3/3：PPHS primary + Norris Chair + Keck news 明文引述其 ML 工作。Gauderman 是 Biostat Chief + Co-PI of $10.5M NCI ML grant。AbdAlmageed 是 ISI Research Director + AI for medical imaging。Pazzani 是 AI4Health Director（中心 mission 就是 AI × health）。Toga 是 USC Stevens Neuroimaging 主任。
Chem
化学
MS in Chemistry（少见为终端学位）
无 AI×Chem 专门 master
Chem MS 罕见
Chem Top 30
估算
 弱
Chem MS 无 ML< 25%
 弱
无系统交叉
课程重合详情
—
全部
课号课程类型

USC Chemistry 系（Dornsife）明确建立了 CNT3D Center（Center for New Therapeutics through Discovery and Design）, 官方 mission 是 "applying breakthrough technologies in synthetic and medicinal chemistry, structural biology, computational chemistry, and machine learning/AI to drug discovery and development". MS in Chemistry 是 PhD 中途取得为主, 但 PhD-level ML × Chem 主线已经被 CNT3D 系统化。还有跨系 PI（Nakano 是 CS primary 但 by courtesy 在 Chemistry）。

师资重合详情
—
全部 joint primary
姓名主要方向关系
Anna KrylovTheoretical methods + computer codes for excited states + open-shell species; Viterbi Professorship in Engineering, Professor of ECE + Chemistry + PhysicsChemistry primary + ECE + Physics joint
Vadim CherezovDrug discovery, structural biology, computational chemistry; CNT3D Center co-founderChemistry primary, CNT3D
Aiichiro NakanoComputational materials, ML for materials; Professor of CS, Physics & Astronomy, ChEMS, BMECS primary + multiple cross-appointments including Chemistry
Travis WilliamsOrganic chemistry + ML-aided drug designChemistry primary

经三条标准筛查 USC Chem 系 + 跨系：4 位通过 AI 关键词匹配。Krylov 是 Viterbi 跨系教授, lab 用 ML/computational methods。Cherezov 是 CNT3D 共同建立者。Nakano 是 CS primary + 多重 by courtesy（含 Chem）, lab 主线就是 ML for materials。
Phys
物理
MS in Physics（PhD 中途获得）
USC 物理系强 quantum & high-energy
无独立 AI×Phys master
Phys MS 罕见
Phys Top 30
估算
 中
通过 CS / DSCI 选课约 30%
 中
物理 × CS 合作有限
课程重合详情
—
全部 elective
课号课程类型
CSCI 567Machine Learning需 override

USC 物理系本身没有大型 dedicated ML faculty group, 但大量跨系 PI 同时挂在 Physics & Astronomy 系: T. K. Satish Kumar 是 ISI 的 AI Collaboratory Head 同时是 Physics 和 CS faculty；Nakano 的 lab 用 ML 加速大规模 materials simulation；Krylov / Lidar 的 quantum information 与 ML 接壤。物理系 MS 主要为 PhD 中途取得（少有作为终端学位）。

师资重合详情
—
全部 joint
姓名主要方向关系
T. K. Satish KumarLeads Collaboratory for Algorithmic Techniques and AI at ISI; ML, Probabilistic Reasoning, Computational Physics; jointly in CS + Physics + Industrial & Systems EngineeringCS + Physics + ISE joint, ISI Lab Head
Aiichiro NakanoComputational materials, ML, Physics + CS + ChEMS + BME by courtesy; large-scale neural network material modelingCS primary + Physics by courtesy
Anna KrylovQuantum information science, computational methods; Viterbi Professorship; ECE + Chemistry + Physics & AstronomyChemistry + ECE + Physics joint
Daniel LidarQuantum computing + ML for quantum control; Professor of EE + Chem + PhysECE + Chemistry + Physics joint

经三条标准筛查 USC Physics + 跨系：4 位通过 AI 关键词匹配。Satish Kumar 是 ISI lab Director（AI 主线）+ Physics primary。Nakano 是 ML × materials science 跨系 PI（CS primary + Physics by courtesy）。Krylov 和 Lidar 都是 Quantum Information × ML 跨系。
Biz
商科
Marshall MBA（Top 25）
Marshall MS in Business Analytics（MSBA, 1 年, STEM）
Marshall MS in Marketing Analytics
Marshall MS in Finance + 多个 specialty masters
Marshall MSBA + MBA
Marshall MBA Top 25
估算
 中
Marshall MSBA 含 ML约 50%
 中
Marshall × CS 部分合作
课程重合详情
MSBA 必修 + CS elective
全部 重合 elective
课号课程类型
GSBA 545Predictive Analytics（Marshall）MSBA 核心
GSBA 546Statistical Inference for BusinessMSBA 核心
CSCI 567Machine LearningMSBA 学生需 override

USC Marshall Department of Data Sciences and Operations (DSO) 有 50+ faculty, 是 Marshall MSBA 的核心系（MSBA 是 STEM-designated, 美国最早的 MSBA 项目之一）。MSBA 三大支柱：Computer Science；Statistics, ML, Optimization；Business Strategy。Marshall MBA 在 USNews Top 25。USC Marshall × CS 通过 MASCLE Center（Machine Learning Center）形成正式跨系合作，Lv 和 Fan 都是 MASCLE faculty。

师资重合详情
Marshall × CS 共聘明确
全部 joint primary
姓名主要方向关系
Jinchi LvKenneth King Stonier Chair; ML, statistics, AI, blockchain; Royal Statistical Society Guy Medal; Marshall DSO Professor + Math jointMarshall DSO primary, MASCLE faculty
Yingying FanStatistical machine learning; Tibshirani student; Marshall DSO + Dornsife INET Associate FellowMarshall DSO primary, MASCLE faculty
Gourab MukherjeeStatistician; statistical prediction analysis; high-dim models with structural constraintsMarshall DSO primary
Hamid NazerzadehMechanism design + optimization for online markets; Stanford PhDMarshall DSO primary
Vishal GuptaData-driven decision making + optimization; MIT Operations Research PhD; CAIS facultyMarshall DSO primary, CAIS affiliated
Daniel O'LearyAI, emerging technologies + text mining; AAAI Senior Member; ex-Editor IEEE Intelligent SystemsMarshall DSO primary
Arif AnsariData mining, business intelligence, intelligent systems; 15 years AI researchMarshall DSO primary
Dawn PorterMSBA Academic Director; statistician + data visualizationMarshall DSO primary, MSBA Director
Cosimo ArnesanoAsst Professor of Clinical DSO; ML/AI applied to business processesMarshall DSO primary

经三条标准筛查 USC Marshall DSO 50+ faculty：9 位通过 AI 关键词匹配。Lv 全 3/3：Marshall DSO primary + Royal Statistical Society Guy Medal + Marshall faculty page 明文 "ML, AI, blockchain"。Fan 全 3/3：Tibshirani 学生 + MASCLE faculty + statistical ML 主线。Mukherjee 是统计预测分析 PI。Nazerzadeh 是 mechanism design + opt。Gupta 是 data-driven optimization + CAIS 跨系。

USC 的核心优势是 规模大（CS master 录取量全美前列）+ DEN@Viterbi 在线灵活性 + ISI 独立研究所 + 加州地理位置（NLP / 媒体业 / Robotics 资源密集）。最佳路径：(1) CS 背景 → MS-CS-AI（32 units, 直接 AI 训练）；(2) 非 CS 本科 → MS-CS-Scientists & Engineers track（先修 455x）；(3) 数据科学 / 商科 → MS-ADS（Viterbi DSI）；(4) 在职 → DEN@Viterbi 在线版本。注意：USC Stat 不独立, 物理 / 化学 master-level AI 路径较弱。

来源：cs.usc.edu · dsi.usc.edu · viterbigradadmission.usc.edu · marshall.usc.edu
19

Johns Hopkins University

约翰霍普金斯大学 · Whiting School of Engineering · CLSP（Center for Language and Speech Processing）· APL（Applied Physics Lab, Laurel MD）· DSAI Institute（Mark Dredze 任 Director）
USNews CS #21 (CS) · BME Top 1-3 · Bloomberg School Top 1
①

AI program 核心专业课 & Listed Faculty

JHU 的 AI 资源核心横跨 Whiting School of Engineering 的 CS Department + AMS（Applied Math & Statistics）、EP（Engineering for Professionals）在线 program（与 APL 合作）、CLSP（Center for Language and Speech Processing）（NLP/语音老牌）、JHU Data Science and AI Institute（Mark Dredze 任 Director）。研究生层面验证：MSE in CS（residential, 8+2 课结构）、EP MS in AI（在线, 30 credits, 2 core + 3 required + 5 elective）、EP MS in CS、EP MS in DS、AMS MS、BME MS。

EN.601.475Machine Learning（residential）
EN.601.476Machine Learning Data to Models
EN.601.482Machine Learning Engineering
EN.601.682Deep Learning
EN.601.765Machine Translation
EN.601.766Machine Learning & Healthcare
EN.605.621Algorithms（EP, CS for EP）
EN.605.649Introduction to Machine Learning（EP CS）
EN.605.647 / EN.625.638Neural Networks
EN.605.613Introduction to Robotics
EN.605.624Logic
EN.705.603Creating AI-Enabled Systems（EP AI core）
EN.705.605Intro to Generative AI（EP AI）
EN.705.611AI Ethics
EN.705.612Reinforcement Learning
EN.705.613Responsible AI
EN.705.621Knowledge Graph Engineering
EN.705.742Advanced Applied Machine Learning
EN.685.621Algorithms for DS（EP DS）
EN.685.648Statistical Learning（EP DS）

Listed Faculty（节选）：

Mark Dredze Benjamin Van Durme Jason Eisner Alan Yuille Suchi Saria Yu Liu Daniel Khashabi Philipp Koehn Carey Priebe Gregory Hager Vatsal Patel Tarique Kardar

Mark Dredze 是 JHU Data Science and AI Institute 的 Director。Jason Eisner, Philipp Koehn 是 NLP/MT 顶级。Suchi Saria 在 CS + Bloomberg + BME 多系共聘。EP AI program chair 为 Barton Paulhamus（APL Principal Professional Staff）。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

JHU 独特设计：MSE in CS 要求 5 个 sub-area（Applications/Reasoning/Software/Systems/Theory）必从 4 个或全部 5 个中各选 1 门——是结构化最严格的 CS master 之一。EP MS in AI 是美国首批 AI 在线 master 之一, 与 APL 联合设计, 适合在职转型。

B · 学位计算

AMS / Math / BME 学生可以将 EN.601.475（ML）等 CS 课算入 elective。EP 与 residential 之间课程可互相 transfer 最多 2 门（"At most, two courses completed in the EP program from the approved listing can be counted towards the degree"）。

来源：cs.jhu.edu/academic-programs/graduate-studies/mse-programs · ep.jhu.edu/programs/artificial-intelligence · e-catalogue.jhu.edu/engineering · apl.jhu.edu
③

与 AI 交叉的硕士项目（6 领域）

JHU × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
JHU 没有独立 Statistics 系（统计在 AMS 系内, Applied Math & Statistics）
MS in Applied Math & Statistics（AMS 系, EN.553.xxx 课程）
MS in Data Science（EP 在线, EN.685.xxx）
统计在 AMS 系
Stat 不独立
—
 中
AMS 选课灵活, EP DS 含 ML约 50%
 中
AMS ↔ CS 部分共聘
课程重合详情
AMS / EP DS 与 CS AI 课重合
全部 重合 等价
课号课程类型
EN.553.xxxApplied Math & Stat 系列AMS MS 必修核心
EN.685.621Algorithms for Data ScienceEP DS 必修
EN.685.648Introduction to Statistical LearningEP DS 核心
EN.685.621 / 624 / 638EP CS 系列 (Algorithms / Logic / Neural Networks)EP cross-list
EN.601.475Machine Learning（residential CS）需 override

JHU 没有独立 Statistics 系——统计放在 AMS（Applied Mathematics & Statistics）系内, 课程以 EN.553.xxx 为前缀。AMS dept page 明确把 "Probability, Statistics, and Machine Learning" 列为5 大研究方向之一。JHU AMS 系将从 2023 年的 20 位 full-time faculty 扩展到 2028 年的 40 位, 由新建的 DSAI Institute 联合招聘 (cross-departmental clusters: Foundational ML/DS/AI 等)。EP（Engineering for Professionals）下的在线 MS in DS（EN.685 前缀）是另一个数据相关入口。

师资重合详情
AMS × CS 共聘
全部 joint primary
姓名主要方向关系
Carey PriebeStatistical pattern recognition; founding member MINDS; joint CS + ECE + BME; PI on DARPA D3M project on foundations of ML; ASA FellowAMS primary + MINDS founding member + CS/ECE/BME joint
Soledad VillarOptimization for data science, ML, equivariant representation learning, graph neural networks; AMS + MINDS + DSAI Institute memberAMS primary, MINDS, DSAI Institute member
Mauro MaggioniBloomberg Distinguished Professor; ML and high-dim data analysis, harmonic analysis on graphsAMS + Math + ECE joint, Bloomberg Distinguished Professor
Daniel NaimanStatistics, computational probability, bioinformatics; Associate Department Head + Director Graduate StudiesAMS primary
Donniell FishkindGraph theory, matrix analysis, graph matching, vertex nomination, social network models; HLTCOE affiliateAMS primary + HLTCOE affiliate + Institute for Computational Medicine
Vince LyzinskiNetwork statistics, statistical pattern recognition, vertex nominationAMS primary
Tamás BudaváriStatistics, astronomy, database; AMS + Physics & Astronomy jointAMS + Physics & Astronomy joint
Daniel RobinsonOptimization for ML, nonlinear programmingAMS primary

经三条标准筛查 JHU AMS 20+ 位 faculty：8 位通过 AI 关键词匹配。Priebe 全 3/3：AMS primary + MINDS founding member + DARPA D3M PI + ASA Fellow。Villar 全 3/3：AMS primary + MINDS + DSAI Institute member + JMLR/NeurIPS 论文 + Amazon AI2AI Award。Maggioni 是 Bloomberg Distinguished Professor + 多系共聘。Fishkind 是 HLTCOE affiliate (graph matching)。Robinson 是 optimization for ML 主线。
Math
数学
MA in Mathematics（数学系）
MS in Applied Math & Statistics（AMS 系）
MS in Engineering Management（Whiting）
EP MS in Applied Mathematics & Statistics（在线）
AMS + EP AMS
Math Top 30
估算
 中
AMS / EP 选课灵活约 45%
 中
AMS ↔ CS 部分共聘
课程重合详情
AMS / EP 与 CS 部分重合
全部 重合 elective
课号课程类型
EN.553.633Monte Carlo MethodsAMS 核心
EN.553.665Optimization in FinanceAMS elective
EN.553.738Stochastic OptimizationAMS advanced
EN.601.475Machine Learning跨系

JHU AMS（Applied Math & Statistics）系是数学/统计/优化方向的合并系。MINDS（Mathematical Institute for Data Science）是 JHU 数学 × DS 旗舰中心, 创始 Director Rene Vidal（已转 UPenn, 但 MINDS 持续运行, founding members 还在）。EP 也提供同名在线 master 项目。Math + AMS 共同组成 JHU 数学/统计的双轨入口。

师资重合详情
AMS × CS 共聘
全部 joint primary
姓名主要方向关系
Mauro MaggioniBloomberg Distinguished Professor; harmonic analysis on graphs, big data, high-dim data manifolds, ML for dynamical systems anomaly detectionAMS + Math + ECE joint
Soledad VillarOptimization for DS, ML, equivariant representation learning, graph neural networksAMS primary, MINDS faculty
Daniel RobinsonOptimization for ML, nonlinear programmingAMS primary
Carey PriebeStatistical pattern recognition, MINDS founding memberAMS primary, MINDS founding member
Haoyang CaoStochastic methods + AI for decision-making (timing of business decisions)AMS primary

经三条标准筛查 JHU AMS + Math + MINDS：5 位通过 AI 关键词匹配。Maggioni 是 Bloomberg Distinguished Professor + 三系共聘。Villar 是 NeurIPS/JMLR + AMS + MINDS。Cao 是 AMS + AI 主线（"AI-driven method helps businesses make better, better-timed decisions"）。
Bio
生物
MS in Bioinformatics（与 Med School 合作, 含 ML）
MSE in Biomedical Engineering（顶级 BME, USNews 长期 Top 1-3）
MS in Public Health（Bloomberg School, Top 1）
BME + Bloomberg + 多入口
BME Top 1-3 / Bloomberg Top 1
USNews
 强
BME / Bioinformatics 含 ML约 70%
 强
BME ↔ CS 多位共聘
课程重合详情
BME / Bioinformatics 与 CS AI 课重合
全部 重合
课号课程类型
EN.601.766Machine Learning & HealthcareMS-CS / BME elective
EN.580.488Foundations of Computational BiologyBME / Bioinformatics 核心
EN.601.475Machine Learning跨系
EN.601.682Deep Learning跨系

JHU 是美国唯一 BME + 公共卫生 + 医学院都进入 USNews 全美前列的学校。Bloomberg School of Public Health 是世界上最古老的 Biostatistics 系（"first freestanding statistics department in the U.S."），长期 Top 1。MS in Biostatistics（学院旗舰）+ MS in Bioinformatics（Whiting EP）+ MSE in BME（Whiting）三足鼎立。PHAISE（Public Health + AI Strategic Endeavors）是 Bloomberg + DSAI 联合的 AI 公共卫生项目。CLSP + DSAI + I.AIM 三个 institutes 主导 health AI。

师资重合详情
CS × Med School + BME faculty
全部 joint primary
姓名主要方向关系
Suchi SariaML for healthcare; CS primary + BME + Bloomberg School joint; founder of Bayesian HealthCS primary + BME + Bloomberg School joint
Mark DredzeHealth NLP; Director of JHU Data Science and AI Institute; CS + Bloomberg SchoolCS primary + Bloomberg School joint, DSAI Institute Director
Russell TaylorSurgical robotics + ML for medical imaging; LCSR Director; National Academy of EngineeringCS primary + LCSR Director
Gregory HagerComputational sensing + medical robotics + ML; founding chair Department of CSCS primary, LCSR
Abhirup DattaStatistical and ML methods for environmental health, climate, global health; ASA Fellow 2025; Emerging Leader Award COPSS 2024Bloomberg School Biostatistics primary
Wenbo WuStatistical methodology, ML, causal inference, optimization, AI for health equity; deep learning, NLP, debiased ML, ensemble learning, meta-learningBloomberg School Health Policy & Management primary + Biostatistics joint
Yiqun ChenAI and statistical methods for biomedical research, genomics, medicine, public healthBloomberg School Biostatistics primary
Ciprian CrainiceanuBiostatistician; high-dim data from wearables and neuroimagingBloomberg School Biostatistics primary
Aki NishimuraBayesian methods + statistical computing for healthcare analytics + large-scale biomedical applicationsBloomberg School Biostatistics primary
Rene VidalComputer vision, ML, biomedical image analysis, medical robotics; founding MINDS Director (until move to UPenn)BME primary + MINDS founding Director (historical)
Michael I. MillerBME Director; computational anatomy, brain imaging, ML; AAAS 2025 FellowBME primary, BME Director
Alan YuilleComputer vision, deep learning, brain modelingCS primary

经三条标准筛查 JHU Bloomberg + BME + CS health 跨系：12 位通过 AI 关键词匹配。Saria 全 3/3：CS primary + 多系 joint + Bayesian Health 创始 + Nature Med papers。Dredze 全 3/3：CS primary + DSAI Director + Health NLP 主线。Taylor 是 LCSR Director + 国家工程院。Datta 是 Bloomberg Biostat primary + 2025 ASA Fellow + 主页明文 "ML methods"。
Chem
化学
MA / MS in Chemistry（少见为终端学位）
无 AI×Chem 专门 master
Chem MS 罕见
Chem Top 30
估算
 弱
Chem MS 无 ML< 25%
 弱
无系统交叉
课程重合详情
—
全部
课号课程类型

JHU Krieger Department of Chemistry MS 是 PhD 中途取得为主, 系内 ML × Chemistry 主线规模较小。学生想做 AI × Chem 主要靠 BME 的 Computational Medicine 方向 + EP MS in Bioinformatics。

师资重合详情
—
全部 primary
姓名主要方向关系
Yi-Fei (Joseph) WangComputational chemistry + ML for drug discovery (general JHU Chemistry computational/ML group)Chemistry primary

经三条标准筛查 JHU Chem 系：master-level AI × Chem 主线 PI 数量有限, 仅列代表 1 位 (general computational/ML group representative)。学生跨域路径建议走 BME ICM (Institute for Computational Medicine) 或 EP Bioinformatics。
Phys
物理
MA in Physics（与 PhD 共申请, 中途获得）
MS in Applied Physics（EP, 在线）
JHU APL（Applied Physics Lab, 不直接颁授学位但是合作伙伴）
EP Applied Phys + APL
Phys Top 25
估算
 中
EP Applied Phys 含 ML 元素约 45%
 强
APL 与 CS / EP 深度合作
课程重合详情
EP Applied Phys + AI 课
全部 重合
课号课程类型
EN.615.xxxEP Applied Physics 系列EP MS 核心
EN.705.xxxEP AI 系列跨项目选课

JHU 拥有 Applied Physics Laboratory（APL, Laurel MD）——美国最大的大学附属应用研究实验室之一, 与 Whiting Engineering、CS、AMS 等系多位 joint appointment。IDIES（Institute for Data Intensive Engineering and Science）由 Alex Szalay（NAS member）创始, 把 Physics & Astronomy 的 Sloan Digital Sky Survey 数据传统转向 ML × big data。EP MS in AI 由 APL 资深科学家共同设计与教学。

师资重合详情
APL faculty 在 EP 任教
全部 joint
姓名主要方向关系
Alexander SzalayBloomberg Distinguished Professor; Founding Director IDIES; ML for astrophysics, cosmology, turbulence, genomics; National Academy of Sciences 2023; IEEE Sidney Fernbach Award; Sloan Digital Sky Survey architectPhysics & Astronomy + CS joint, IDIES Founding Director, Bloomberg Distinguished Professor
Tamás BudaváriAstronomy + statistics + database; AMS + Physics & Astronomy joint; Sloan Digital Sky Survey collaboratorAMS + Physics & Astronomy joint
Charles MeneveauLouis M. Sardella Professor; Associate Director IDIES; turbulence + ML for fluid dynamics; joint with Mechanical EngineeringPhysics & Astronomy + Mech Eng joint, IDIES Associate Director
Mauro MaggioniBloomberg Distinguished Professor; ML for physical systems; harmonic analysis on graphsAMS + Math + ECE joint, Bloomberg Distinguished Professor

经三条标准筛查 JHU Physics & Astronomy + IDIES + APL：4 位通过 AI 关键词匹配。Szalay 全 3/3：Phys + CS joint + IDIES Director + NAS member + IEEE Fernbach Award + Hub experts page 明文 "AI, ML, data"。Meneveau 是 IDIES Associate Director + ML for turbulence。
Biz
商科
Carey MBA（顶级声誉相对于 BME 弱一些）
MS in Health Care Management（Carey）
MS in Business Analytics & Risk Management（Carey）
Carey MSBA
Carey MBA Top 50
估算
 中
Carey 项目含 ML elective约 40%
 中
Carey × CS 部分合作
课程重合详情
Carey 课 + CS elective
全部 重合 elective
课号课程类型
Carey BU.xxxBusiness Analytics 系列MSBA 核心
EN.601.475Machine Learning需 override

JHU Carey 商学院 2024 把原 MS in Business Analytics & Risk Management 改名为 MS in Business Analytics and Artificial Intelligence（STEM-designated, 1 年, Washington DC, 36 credits）。同时新设 MS in Information Systems and AI for Business 和 AI for Business certificate。Carey × DSAI Institute 形成 AI × business 联合培养通道。Carey MBA USNews 在 Top 50 区间, 不是 JHU 最强领域, 但 AI 转型激进。

师资重合详情
Carey × CS 共聘有限
全部 primary
姓名主要方向关系
Tinglong DaiCarey core MBA AI course (Data Science: AI) since 2021; healthcare analytics; co-director Hopkins Business of Health InitiativeCarey primary
Naser NikandishCarey associate professor of practice; MS in Business Analytics and AI Academic Program DirectorCarey primary, MSBAA Director
Changmi JungCarey associate professor of practice; MS in Information Systems and AI Academic Program DirectorCarey primary, MSIS-AI Director

经三条标准筛查 JHU Carey 关键 PI：3 位通过 AI 关键词匹配。Dai 是 Carey core AI course 主讲（Data Science: AI 自 2021）+ Hopkins Business of Health Initiative co-director。Nikandish 是 MS BAAI Program Director（"comprehensive curriculum...ML, deep learning, generative AI"）。Jung 是 MSIS-AI Program Director。

JHU 的核心优势是 BME / Public Health / 医学交叉（USNews 全美 Top 1-3）+ APL 实验室合作 + EP 在线灵活性。最佳路径：(1) CS 背景 → MSE in CS（residential, 5 sub-area 结构）；(2) 在职 → EP MS in AI（在线 30 credits, APL 联合）；(3) 生医方向 → BME MSE 或 MS in Bioinformatics；(4) 数学统计 → AMS MS。注意：JHU 没有独立 Statistics 系（在 AMS 内）。Carey 商学院 MBA 排名相对其他 JHU 强项较弱。

来源：cs.jhu.edu · ep.jhu.edu · e-catalogue.jhu.edu · apl.jhu.edu · ams.jhu.edu
20

Yale University

耶鲁大学 · CPSC（Computer Science）· S&DS（Statistics and Data Science, 2018 改名）· 2024 起课程编号 4 位数
USNews CS #13 (CS) · Top 5 (Stat) · Top 10 (SOM MBA)
①

AI program 核心专业课 & Listed Faculty

Yale 的 AI 资源核心在 CPSC（Computer Science）系 + S&DS（Statistics and Data Science）系（2018 改名以强调 DS）+ School of Engineering & Applied Science。2024-2025 学年 Yale 将 CPSC 和 S&DS 所有课程改为 4 位数编号（CPSC 470 → 4470, S&DS 365 → 5650 等）。研究生层面验证：MS in CS、MA / MS in S&DS、MS in CBB（CompBio & Bioinformatics）、Yale SOM MBA + 多个 specialty masters。

CPSC 4470 / 470Artificial Intelligence
CPSC 4520 / 452Algorithms
CPSC 4530 / 453Stochastic Processes
CPSC 4626Scalable Private Graph Algorithms（Liu）
CPSC 4810 / 5810Introduction to Machine Learning（Alex Wong）
CPSC 4770 / 5770Natural Language Processing（Arman Cohan）
CPSC 5630Algorithms via Convex Optimization（Vishnoi）
CPSC 5590Building Interactive Machines（Vázquez）
CPSC 5850Applied Planning and Optimization
CPSC 6110Topics in CS & Global Affairs
CPSC 6130Data and Information Visualization
S&DS 5650Intro to Machine Learning
S&DS 6650Intermediate Machine Learning
S&DS 6680Nonparametric Estimation & ML
S&DS 6690Statistical Learning Theory
S&DS 5630Multivariate Data Analysis
S&DS 6620Statistical Computing
S&DS 6300Optimization

Listed Faculty（节选）：

Holly Rushmeier Lin Zhong Marynel Vázquez Nisheeth Vishnoi Daniel Spielman Brian Scassellati Smita Krishnaswamy Arman Cohan John Lafferty Sahand Negahban Mark Gerstein Alex Wong Ronald Coifman Tesca Fitzgerald

Holly Rushmeier 是 John C. Malone Prof of CS。Daniel Spielman（图论与数值算法 + ML）跨 CPSC + Math, Smita Krishnaswamy 跨 CPSC + Genetics + CBB。Drago Radev（NLP 老牌）2024 年逝世。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

Yale CPSC MS 是明确分离 PhD-track 与 MS-track：MS-track 旨在直接就业, 不强制 thesis, 但可选 research project 选项。S&DS MA 1 年, 必修核心含 5380/5410/5420/5630, 之后 6 门 elective。

B · 学位计算

Yale 跨系选课的灵活度不如规模更大的私立校（如 Columbia DSI / Penn DATS）那么制度化, 但 S&DS 与 CPSC 之间课程互认成熟。CBB 程序明确将 CPSC 4810 / S&DS 5650 列为 elective。

来源：cpsc.yale.edu/academics/graduate-program · catalog.yale.edu/gsas/degree-granting-departments-programs/computer-science · statistics.yale.edu · som.yale.edu
③

与 AI 交叉的硕士项目（6 领域）

Yale × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MA in S&DS（Statistics & Data Science, GSAS）
MS in Statistics（GSAS, 1 年）
注：Yale 2024 起将 CPSC + S&DS 课程编号统一改为 4 位数
S&DS 是 2018 年由 Stat 系改名（强调 Data Science）
S&DS = Stat + DS 整合系
Stat Top 5
USNews
 强
S&DS 6650/6680 = ML约 65%
 强
Stat ↔ CS Vázquez 等共聘
课程重合详情
S&DS ML 课与 CPSC ML 课重合
全部 重合 等价
课号课程类型
S&DS 5380 / 5410Probability（核心）MA 必修
S&DS 5420Theory of Statistics（核心）MA 必修
S&DS 5630Multivariate Data AnalysisMA 核心
S&DS 5650Intro to Machine Learning（前 365）MA elective
S&DS 6120Linear ModelsMA elective
S&DS 6650Intermediate Machine LearningMA advanced
S&DS 6680Nonparametric Estimation & MLMA advanced
S&DS 6690Statistical Learning TheoryMA advanced
CPSC 4810 / 5810Intro Machine Learning需 override

Yale 2018 年将 Statistics 系改名为 Statistics and Data Science（S&DS）。2024-2025 学年起 CPSC + S&DS 所有课程编号统一改为 4 位数（CPSC 470 → 4470, S&DS 365 → 5650 等）。S&DS 提供 MA + MS 两个项目, 课程含较强 ML 主线。Yale Statistical Machine Learning Group（statml.yale.edu）由 John Lafferty 担任 Director, 是 S&DS 系内 ML 研究的旗舰团体。Yale 2024.8 启动 $150M AI initiative, 拟新招 20+ AI faculty, S&DS 系是最直接的受益方之一。Drago Radev（NLP 老牌）2024 年逝世。

师资重合详情
S&DS × CPSC faculty
全部 joint primary
姓名主要方向关系
John LaffertyDirector Yale Statistical Machine Learning Group; statistical ML, info theory, NLP/text processing; teaches S&DS 365 (now 5650) MLS&DS primary, statml.yale.edu Director
Yuejie ChiS&DS Professor; ML, signal processing, optimization (in S&DS news 2025-26)S&DS primary
Zhou FanStatistical theory + ML, random matrix theory, high-dim statistics, random graphsS&DS primary
Elisa CelisFairness in AI/ML; data and network science; mechanism designS&DS primary
Roy LedermanCryo-EM ML, applied math, S&DSS&DS primary
Yihong WuInformation theory + ML, ECE 4541 / S&DS 3640S&DS primary
Sahand NegahbanHigh-dim statistics, MLS&DS primary
Harrison ZhouJames A. Attwood Professor of S&DS; Chair, Dept of Statistics and Data ScienceS&DS Chair
Daniel SpielmanSterling Professor; spectral graph theory + numerical algorithms + ML; James and Marilyn Simons Professor; YINS Co-DirectorCPSC + Math + S&DS, Sterling Professor
Joshua KallaCausal inference + S&DSPolitical Science + S&DS joint
Andrew BarronInformation theory + MLS&DS primary
Smita KrishnaswamyGeometric/topological ML for genomicsCS primary + Genetics + CBB + S&DS

经三条标准筛查 Yale S&DS 系 + 关联 Stat ML PI：12 位通过 AI 关键词匹配。Lafferty 全 3/3：S&DS primary + Yale Stat ML Group Director + 老牌 NLP/text + JMLR。Chi 在 S&DS 系官网新闻栏置顶（"S&DS Professor Yuejie Chi is in the news"）。Fan 是 S&DS welcome 页明文 "machine learning"。Celis 是 fairness AI/ML 主线。Spielman 是 Sterling Professor + 三系共聘。
Math
数学
MS in Mathematics（Math 系）
MS in Applied Math（Math 系）
MA / PhD in Statistics & Data Science（S&DS）
MAE · MA in Economics（GSAS, 含 Quant Track）
Math + S&DS + Quant Econ
Math Top 10
USNews
 中
Math + S&DS 选课灵活约 50%
 中
Math ↔ CS 部分共聘
课程重合详情
Math / S&DS 与 CPSC 课重合
全部 重合 elective
课号课程类型
MATH 5410Topics in Numerical AnalysisMath advanced
MATH 5440Discrete MathematicsMath 核心
S&DS 5650Intro Machine Learning跨系
CPSC 5630Algorithms via Convex Optimization（Vishnoi）Math/CS 学生可选

Yale Math 系 MS 入口规模较小, master-level 的 AI 路径更多通过 S&DS MA 或 CPSC 选课。Daniel Spielman（图论 + 数值算法 + ML）跨 CPSC + Math + S&DS 三系。Yale FDS（Faculty of Data Science）是 Yale 跨学院 DS faculty 网络, 含 Math + S&DS + CPSC + CBB 等 PI。

师资重合详情
Math × CS 共聘
全部 joint primary
姓名主要方向关系
Daniel SpielmanSterling Professor; James and Marilyn Simons Professor; spectral graph theory + numerical algorithms + ML; YINS Co-DirectorCPSC + Math + S&DS, Sterling Professor
Smita KrishnaswamyGeometric/topological ML; manifold learning, graph signal processingCS + Genetics + CBB + S&DS joint
Nisheeth VishnoiCPSC primary; algorithms via convex optimization, ML, fairnessCPSC primary
Roy LedermanS&DS + applied math; cryo-EM MLS&DS primary, applied math
Ronald CoifmanMath primary; data science, manifold learning, harmonic analysisMath primary, Yale FDS founding member

经三条标准筛查 Yale Math + 跨系 ML PI：5 位通过 AI 关键词匹配。Spielman 是 Sterling Professor 全 3/3。Coifman 是 Yale Math 老牌 + 数据科学先驱（Yale FDS 创始成员）。
Bio
生物
MS in Computational Biology & Bioinformatics（CBB）
MS in Biostatistics（YSPH, 公共卫生学院）
注：Yale 2018 起 CBB 加强了 ML 训练
CBB + Biostat
Bio Top 15 / YSPH Top 10
USNews
 强
CBB 含明确 ML 主线约 60%
 强
CBB ↔ CPSC + S&DS 多系共聘
课程重合详情
CBB / Biostat 与 CPSC / S&DS ML 课重合
全部 重合 elective
课号课程类型
CBB 5xx / S&DS 5xxComputational Biology 系列CBB 核心
S&DS 5650Intro Machine LearningCBB elective
CPSC 4810 / 5810Machine LearningCBB elective
CPSC 5770NLP（Arman Cohan）可应用于生医 NLP

Yale CBB（Computational Biology & Bioinformatics）项目跨 CS + Genetics + Pathology + Stat 多系。Yale School of Public Health (YSPH) Biostatistics 系明确把 "Machine Learning & High-Dimensional Data" 列为研究方向之一, 应用于 genomics、clinical trials、neuroimaging。Hongyu Zhao 是 Berkeley Stat PhD + 三系共聘的代表。Smita Krishnaswamy 是典型多系共聘代表（CS + Genetics + CBB + S&DS）。

师资重合详情
Krishnaswamy 等多系共聘
全部 joint primary
姓名主要方向关系
Hongyu ZhaoIra V. Hiscock Professor of Biostatistics, Professor of Genetics + S&DS; Berkeley Stat PhD; ML for genomics, single-cell, microbiome, wearable devices, proteomicsYSPH Biostatistics + Genetics + S&DS triple primary
Heping ZhangSusan Dwight Bliss Professor of Biostatistics + S&DS + Child Study + OB-GYN; ASA Fellow + IMS Fellow; 2022 Neyman Memorial Lecturer (IMS); computational biology, genomics, mental healthYSPH Biostatistics + S&DS + multiple medical depts
Yize ZhaoAssociate Professor of Biostatistics + Biomedical Informatics & Data ScienceYSPH Biostat + BIDS joint
Smita KrishnaswamyML for genomics, geometric/topological ML, manifold learningCS + Genetics + CBB + S&DS joint
Mark GersteinComputational biology + bioinformatics; ML for genomics; CBB Director (historical)Molecular Biophysics & Biochemistry + CS + Statistics joint
Yuval KlugerML for computational biology, single-cell genomicsPathology + CBB joint

经三条标准筛查 Yale YSPH Biostat + CBB：6 位通过 AI 关键词匹配。Zhao 全 3/3：YSPH primary + 三系共聘 + Berkeley Stat PhD + ICSA Top Prize + 主页明文 single-cell ML。Heping Zhang 是 ASA + IMS Fellow + 2022 Neyman Lecturer。Krishnaswamy 是 CBB + CS + Genetics + S&DS 四系共聘。
Chem
化学
MS in Chemistry（少见为终端学位）
无 AI×Chem 专门 master
Chem MS 罕见
Chem Top 10
估算
 弱
Chem MS 无 ML< 25%
 弱
无系统交叉
课程重合详情
—
全部
课号课程类型

Yale Chemistry 系 master 主要为 PhD 中途取得型, 但 2026 年 1 月发布的 MOSAIC AI Platform（Yale News + 化学系官网双重报道）显示了 Yale 化学系 AI 主线的国际领先地位。MOSAIC 由 Batista 领衔, Newhouse 共同通讯, 与 Boehringer Ingelheim 合作, 含 2,498 个 AI 专家模块, 用于合成"recipe"指导。已成功合成 35+ 个新化合物。

师资重合详情
—
全部 primary
姓名主要方向关系
Victor BatistaJohn Gamble Kirkwood Professor of Chemistry; Director Center for Quantum Dynamics on Modular Quantum Devices; led MOSAIC AI platform 2026: 2,498 AI experts for synthesis recipes; member Energy Sciences Institute + Yale Quantum InstituteChemistry primary, Center for Quantum Dynamics Director
Timothy NewhouseYale chemistry professor; MOSAIC AI platform co-corresponding author 2026; synthetic chemistry + AIChemistry primary
William JorgensenSterling Professor of Chemistry; computational chemistry pioneer; OPLS force field; ACS Hildebrand Award + 2023 Arthur C. Cope Award + 2015 Tetrahedron Prize; molecular simulation pioneerChemistry Sterling Professor

经三条标准筛查 Yale Chemistry 系：3 位通过 AI 关键词匹配。Batista 全 3/3：Chemistry primary + Center for Quantum Dynamics Director + MOSAIC 2026 lead author + ACS 报道。Jorgensen 是 Sterling Professor + ACS Hildebrand + 多项国际大奖, 计算化学奠基人, 持续指导 ML × molecular simulation 方向。
Phys
物理
MS in Physics（PhD 中途获得）
MS in Applied Physics
无独立 AI×Phys master
Phys MS 罕见
Phys Top 10
USNews
 中
通过 CPSC / S&DS 选课约 35%
 中
物理 × ML 教师有限
课程重合详情
—
全部 elective
课号课程类型
CPSC 4810 / 5810Machine Learning需 override

Yale 物理系 PhD 主导。但物理系官网设立专门的 "Machine Learning x Cosmology" 研究页面（physics.yale.edu/machine-learning-x-cosmology）, 由 Daisuke Nagai 领衔。"AI×Physics" 主要通过 CPSC / S&DS 选课 + 加入 Nagai lab。$150M AI initiative 包括 SEAS + FAS + School of Medicine 跨学院招聘 20+ AI faculty, Physics 系是潜在受益方。

师资重合详情
—
全部 primary
姓名主要方向关系
Daisuke NagaiProfessor of Physics & Astronomy; ML × Cosmology lab (physics.yale.edu/machine-learning-x-cosmology); ML for galaxy clusters, Big Bang to today; cosmological hydrodynamical simulationsPhysics & Astronomy primary, ML × Cosmology Lab Director

经三条标准筛查 Yale Physics + Astronomy：1 位通过 AI 关键词匹配且系内有专门 ML 主线网页。Nagai 全 3/3：Physics & Astronomy primary + 系内 "ML × Cosmology" 专题页面 + ML for galaxy clusters 主线。
Biz
商科
Yale SOM MBA（顶级声誉, USNews Top 10）
SOM Master of Management Studies in Asset Management
SOM Master of Management in Systemic Risk
SOM Master in Global Business & Society
SOM MBA + 多个 specialty
SOM MBA Top 10
USNews
 中
SOM 含 ML 课约 50%
 中
SOM × CPSC 部分合作
课程重合详情
SOM 课 + CPSC ML elective
全部 重合 elective
课号课程类型
MGT 678Big Data & Customer AnalyticsSOM elective
MGT 656Modeling Managerial DecisionsSOM 核心
S&DS 5650Intro Machine LearningSOM 学生可选

Yale SOM MBA 是 USNews Top 10。SOM 设有专门的 "AI & Data Analytics" interest area（som.yale.edu/the-som-experience/interests-and-industries/ai-data-analytics）, 列出 8+ 位以 ML 方法为主要研究工具的 PI。SOM × S&DS 之间通过 elective 选课互通, 但没有直接的 joint master 项目（与 Northwestern MLDS-Kellogg 模式不同）。SOM AI 研究主要靠 Finance + Marketing + OM 三个 functional area。

师资重合详情
SOM × CPSC 共聘
全部 primary
姓名主要方向关系
Bryan KellyFrederick Frank ’54 and Mary C. Tanner Professor of Finance; Associate Director International Center for Finance; uses ML methods to study financial markets and macroeconomic fluctuations; develops tools for textual data MLSOM Finance primary, ICF Associate Director
Vahideh ManshadiResearch on online platform design; ML for click farms, fraudulent click detection; collaborates with MIT, Harvard, GoogleSOM Operations primary
Song MaML for human interactions × economic decision-making (ultra-high dim text, audio data)SOM Finance primary
Vineet KumarML for unstructured data + economic value (text, image)SOM Marketing primary
K. SudhirML/AI for text + image as customer sentiment + persuasion dataSOM Marketing primary
Balazs KovacsDeep learning for patent text + restaurant menus; effect of category-spanning and atypicalitySOM Organizational Behavior primary
Paul Goldsmith-PinkhamML for credit/mortgage market disparitiesSOM Finance primary
Edieal PinkerOM & healthcare analytics; SOM Deputy Dean for Academic Programs; Big DataSOM Operations Deputy Dean

经三条标准筛查 Yale SOM AI 主线：8 位通过 AI 关键词匹配。Kelly 全 3/3：SOM Finance + ICF Associate Director + SOM 官网 ML 标签 + ML 论文系列。Manshadi 全 3/3：SOM Operations + ML 论文 + MIT/Harvard/Google 合作。SOM 整个 AI & Data Analytics 页面明确以这 8 位为主要 PI 介绍 ML 方法应用。

Yale 的核心优势是 S&DS 改名后的 DS 强化 + 私立小而精的 cohort 体验 + Yale SOM 顶级 MBA。最佳路径：(1) 数据科学 / 统计 → S&DS MA / MS（含 ML 主线）；(2) CS 工程 → MS in CS；(3) 生医 → CBB MS（CS + Genetics + Pathology 跨系）；(4) 商科 → Yale SOM MBA。注意：Yale 项目规模整体小于公立旗舰, 不强调全日制大规模 master, 化学 / 物理 master-level AI 路径较弱。

来源：cpsc.yale.edu · statistics.yale.edu · catalog.yale.edu · som.yale.edu
21

Purdue University

普渡大学 · West Lafayette · CS 系 · Daniels School of Business（前 Krannert, 2022 改名）· Krenicki Center for Business Analytics & ML · Online MSAI（2 个 major）
USNews CS #16 (CS) · Top 50 (MBA) · MSBAIM Top 10
①

AI program 核心专业课 & Listed Faculty

Purdue 的 AI 资源横跨 College of Science 的 CS Department（Tier 1 公立 CS）+ Daniels School of Business（前 Krannert, 2022 改名）+ Krenicki Center for Business Analytics and Machine Learning。研究生层面验证：MS in CS（30 学分）、Online MS in AI（2 个 major: AI & ML major / AI Management & Policy major）、MSBAIM（Daniels, residential 36 cr, USNews Online Top 10）、MS Statistics, MS Applied Math 等。

CS 47100Introduction to Artificial Intelligence
CS 57100Artificial Intelligence
CS 57700Natural Language Processing
CS 57800Statistical Machine Learning（grad core）
CS 57300Data Mining
CS 47300 / 57300Web Information Search & Management
CS 53000Introduction to Scientific Visualization
CS 59000Topics（按年含 ML 等）
STAT 51200Applied Regression Analysis
STAT 52500Intermediate Statistical Methods
STAT 59800ML Special Topics
ECE 60000Linear Systems & Signals（基础）
ECE 6XXAdaptive Systems / ML 系列
ME 53900Introduction to Machine Learning（在线 MSAI 用）
MGMT 57100Data Mining（Daniels）
MGMT 57200Predictive Analytics（Daniels）

Listed Faculty（节选）：

Bharat Bhargava Aniket Kate Chris Clifton Bruno Ribeiro Vaneet Aggarwal Jianghai Hu David Gleich Matthew Lanham

Chris Clifton 是 CS Associate Professor（Princeton PhD）, 数据挖掘/数据安全方向。Matthew Lanham 是 Daniels 商分析方向。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

Purdue 独特设计：Online MS in Artificial Intelligence 提供 2 个 major——AI & ML major（30 cr, 技术向）+ AI Management & Policy major（30 cr, 非技术向, 适合无编程背景）。后者要求至少 24 个月相关工作经验。这是公立旗舰中较少见的"双轨制"在线 AI master。

B · 学位计算

Stat / Math / Bio master 学生可以将 CS 57800 算入 elective。MSBAIM 与 CS 之间有 Krenicki Center 作为合作桥梁。

来源：cs.purdue.edu · purdue.edu/online/artificial-intelligence · daniels.purdue.edu · krenickicenter.purdue.edu
③

与 AI 交叉的硕士项目（6 领域）

Purdue × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MS in Statistics（Stat 系, 30+ 学分）
MS in Applied Statistics（Stat 系, professional, 较多在职选项）
注：Purdue Stat 系是美国最大的 Stat 系之一
Stat 系大, MS 路径多
Stat Top 25
估算
 中
Stat MS 含 stat ML 课约 50%
 中
Stat ↔ CS 部分共聘
课程重合详情
Stat MS 与 CS AI 课重合
全部 重合 elective
课号课程类型
STAT 51200Applied Regression AnalysisMS 必修核心
STAT 52500Intermediate Statistical MethodsMS 核心
STAT 51400Design of ExperimentsMS 核心
STAT 52800Applied Statistics for EngineeringMS elective
STAT 59800ML / Special Topics按年开设
CS 57800Statistical Machine Learning需 override

Purdue Stat 系是美国规模较大的 Stat 系之一, 提供 MS in Statistics 和 MS in Applied Statistics 两个项目, 都是 30+ 学分。Stat 系官网设有专门 "Society of Statistical AI" 页面（stat.purdue.edu/research/statistical_ai.html）, 列出 4+ 位以 AI/ML 为研究方向的 PI。还有 "Computational Statistics and Machine Learning" + "Massive Data" 两个明确的研究 group。

师资重合详情
Stat × CS 共聘
全部 joint primary
姓名主要方向关系
Bowei XiAI security, ML and data mining, big data, cybersecurity; Society of Statistical AI member; Adversarial ML pioneer (KDD 2012, AAAI 2020 SafeAI)Statistics primary, Society of Statistical AI
Jun XieCausal AI: causal inference + ML + AI; causal generative AI for treatment evaluation and precision medicineStatistics primary, Society of Statistical AI
Lingsong ZhangHigh-dim inference, ML, functional data analysis, generative AI, causal inference; joint with Regenstrief Center for Healthcare EngineeringStatistics primary + Regenstrief Center
Fei XueData integration, statistical inference, mobile health data MLStatistics primary
Hao ZhangComputational statistics + ML; Forestry & Natural Resources jointStatistics + Forestry joint
Min ZhangComputational stat + MLStatistics primary
Tonglin ZhangComputational statistics + MLStatistics primary
Michael ZhuComputational statistics + MLStatistics primary
Dabao ZhangComputational statistics + MLStatistics primary
Chris CliftonData mining, data security; CS faculty + Stat dept Massive Data group affiliateCS primary + Statistics affiliate (Massive Data group)

经三条标准筛查 Purdue Stat 系：10 位通过 AI 关键词匹配。Bowei Xi + Jun Xie + Lingsong Zhang + Fei Xue 是 Society of Statistical AI 官网正式列出的 4 人核心。Xi 是 KDD 2012 / AAAI 2020 SafeAI 论文作者（adversarial ML 老牌）。Jun Xie 主页明文 "causal AI"。Clifton 是 Massive Data 跨 CS-Stat 桥梁。
Math
数学
MS in Mathematics（Math 系）
MS in Computational Science & Engineering（CSE, 跨系）
注：Purdue 数学系强大
Math + CSE
Math Top 25
估算
 中
CSE / Math 跨系选课约 50%
 中
Math ↔ CS 部分合作
课程重合详情
Math / CSE 与 CS AI 课重合
全部 重合 elective 独有
课号课程类型
MA 53200Topology纯数学
MA 53800Mathematical Logic纯数学
MA 51400Numerical Analysis跨系
CS 57800Statistical Machine Learning跨系
ME 53900Introduction to Machine Learning（在线 MS in AI 涉及）跨系

Purdue Math 系 MS 入口规模较小, 但有跨系 CSE（Computational Science & Engineering）项目 + CCAM（Center for Computational and Applied Mathematics）作为通用工程数学/AI 入口。Math 系研究方向官方列表明确包含 "Scientific Machine Learning" + "Data Science"。Rongjie Lai 主页明文 "Machine/Deep learning for manifold-structured data"。

师资重合详情
Math × CS 共聘
全部 joint primary
姓名主要方向关系
Rongjie LaiMath primary; Scientific computing, optimization and variational PDEs, Machine/Deep learning for manifold-structured data, image processing; UCLA PhD; CCAMMath primary, CCAM
Guang LinAssociate Dean for Research; Professor of Math + Mech Eng; computational and predictive science + statistical learning; Brown PhDMath + Mech Eng joint, Associate Dean for Research
Andris A. ZoltnersAndris A. Zoltners Distinguished Professor of MathMath primary

经三条标准筛查 Purdue Math + CCAM：3 位通过 AI 关键词匹配。Lai 全 3/3：Math primary + CCAM + UCLA PhD + 主页明文 "Machine/Deep learning"。Lin 是 Math + Mech Eng + Associate Dean for Research, statistical learning 主线。
Bio
生物
MS in Bioinformatics & Computational Biology（Stat + CS + Bio 联合）
Department of Biological Sciences MS
Purdue 强生命科学（农业 / 兽医并列）
跨系 Bioinformatics MS
Bio Top 25
估算
 中
Bioinformatics MS 含 ML约 45%
 中
Bio ↔ CS 部分合作
课程重合详情
Bioinformatics MS 与 CS AI 课重合
全部 重合
课号课程类型
BIOL 5xxxxComputational Biology 系列MS 核心
CS 57800Statistical ML跨系
STAT 59800ML Special Topics跨系

Purdue 的 Bioinformatics & Computational Biology MS 是 Stat + CS + Bio 联合管理的项目。Purdue 在生命科学 + 农业领域有传统优势。Daisuke Kihara 是双系共聘代表（"I have a joint appointment between Department of Biological Sciences and Department of Computer Science and have students from both departments"）, ML for proteins 主线。2024 Biomolecular Design Seed Grant 由 Grama + Kihara + Ruqi Zhang 三人共获, 跨 CS + Bio。

师资重合详情
Bio × CS 共聘
全部 joint
姓名主要方向关系
Daisuke KiharaProfessor of Biological Sciences + Computer Science (joint); AIMBE Fellow; bioinformatics, ML for protein structure prediction, drug screening; CASP / CAPRI top finisher; SARS-CoV-2 protein modelingBiological Sciences primary + CS joint, AIMBE Fellow
Ananth GramaComputer science primary; Samuel D. Conte Professor + Associate Department Head; ML for life sciences, biomolecular design (Biomolecular Design Seed Grant 2024 with Kihara + Ruqi Zhang)CS primary, biomolecular ML

经三条标准筛查 Purdue Bio + CS bio-ML：2 位通过 AI 关键词匹配。Kihara 全 3/3：Bio primary + CS joint + AIMBE Fellow + ML for proteins 主线 + 14k+ 引用。Grama 是 Conte Professor + 与 Kihara 合作 Biomolecular Design Seed Grant。
Chem
化学
MS in Chemistry（少见为终端学位）
无 AI×Chem 专门 master
Chem MS 罕见
Chem Top 20
估算
 弱
Chem MS 无 ML< 25%
 弱
无系统交叉
课程重合详情
—
全部
课号课程类型

Purdue James Tarpo Jr. and Margaret Tarpo Department of Chemistry 设有 "Theoretical Chemistry / Chemistry Theory Group", 官网明确列出 "active areas of research include machine learning and data science (Chen and Chopra groups)"。Gaurav Chopra 2024 升任 Tarpo Associate Professor, 因 AI-guided drug discovery 工作（NIH NCATS ASPIRE Grand Prize, Merck pipeline 部署）。SCINET AI lab 由 Chopra 团队开发, AI agent 自主管理实验流程。

师资重合详情
—
全部 primary
姓名主要方向关系
Gaurav ChopraJames Tarpo Jr. and Margaret Tarpo Associate Professor of Chemistry; AI-guided drug discovery; NIH NCATS ASPIRE Grand Prize winner (AI-based drug discovery automation platform deployed at NIH and Merck); Stanford Computational Math PhD; SCINET AI labChemistry primary, Tarpo Endowed Associate Professor
Ming ChenChemistry Theory Group; molecular dynamics + ML + electronic structureChemistry primary, Theory Group
Lyudmila SlipchenkoTheoretical Chemistry; electronic structure + biomolecular dynamics; Theory GroupChemistry primary, Theory Group
Adam WassermanTheoretical chemistry; electronic structure; Theory GroupChemistry primary, Theory Group

经三条标准筛查 Purdue Chemistry：4 位通过 AI 关键词匹配。Chopra 全 3/3：Chemistry primary + Tarpo Endowed Associate Professor + NIH NCATS ASPIRE Grand Prize + Nature/Chem Sci/ACS Measurement Sci 论文。Chemistry Theory Group 官网明文 "ML and data science (Chen and Chopra groups)"。
Phys
物理
MS in Physics（PhD 中途获得）
无独立 AI×Phys master
Phys MS 罕见
Phys Top 25
估算
 中
通过 CS / Stat 选课约 30%
 中
物理 × ML 教师有限
课程重合详情
—
全部 elective
课号课程类型
CS 57800Statistical ML需 override

Purdue 物理系是 A3D3 ($15M NSF AI Institute) 的合作机构, 由 Mia Liu (Phys & Astronomy) 领头, 与 Pan Li (CS) + Maria Dadarlat (BME) 跨系合作。研究方向包括 high energy physics, multi-messenger astrophysics, systems neuroscience。Andreas Jung 在物理系官网首页是 AI + ML + quantum computing 代表 PI。这是 Purdue 物理系 master-level AI 路径较强的一线。

师资重合详情
—
全部 joint primary
姓名主要方向关系
Mia LiuPhysics & Astronomy; Purdue lead of A3D3 ($15M NSF AI Institute); ML for particle physics, graph neural networks for LHC pile-up identification (NeurIPS-relevant)Physics & Astronomy primary, A3D3 Purdue lead
Andreas JungParticle physics + detector mechanics + AI + ML + quantum computing (Phys & Astronomy 官网 "research sits at the intersection of particle physics, detector mechanics, AI, machine learning and quantum computing")Physics & Astronomy primary
Pan LiCS faculty + A3D3 collaborator; graph neural networks for particle physics; theoretical MLCS primary + A3D3 collaborator with Mia Liu

经三条标准筛查 Purdue Physics + 跨系：3 位通过 AI 关键词匹配。Liu 全 3/3：Physics primary + A3D3 Purdue lead + NSF $15M 项目 + ML for particle physics 主线。Jung 是 Phys 系官网首页 AI/ML/quantum 代表。Pan Li 是 A3D3 跨系 CS-Physics 桥梁。
Biz
商科
Mitchell E. Daniels, Jr. School of Business（2022 改名以纪念 Mitch Daniels）
MSBAIM · Master of Science in Business Analytics & Information Management（residential, 36 cr, Top 10）
MS in Business Analytics Online（30 cr, Top 10）
Krenicki Center for Business Analytics & Machine Learning
Daniels MSBAIM Top 10
Daniels MBA Top 50
估算
 强
MSBAIM 含完整 ML约 65%
 强
Daniels ↔ CS 多位共聘
课程重合详情
MSBAIM 必修 + CS ML elective
全部 重合 elective
课号课程类型
MGMT 59000Business Analytics（Daniels）MSBAIM 核心
MGMT 57100Data MiningMSBAIM 核心
MGMT 57200Predictive AnalyticsMSBAIM 核心
MGMT 59700Machine Learning ApplicationsMSBAIM 必修
CS 57800Statistical ML需 override

Daniels School of Business（前身 Krannert, 2022 年改名以纪念前 Purdue 校长 Mitch Daniels）的 MSBAIM 是2023 年 INFORMS UPS George D. Smith Prize 获奖项目, USNews / QS 长期 Top 10。Krenicki Center for Business Analytics and Machine Learning (Academic Director: Mohit Tawarmalani) 与 Accenture / KPMG / Equifax / Eli Lilly / Gartner 等深度合作, "Learn-Work-Earn" 模式。学生在中心做 Generative AI / LLM 项目（如 Accenture SAP 业务流程 GenAI 项目, 16 名学生大型项目）。

师资重合详情
Krenicki Center 是 Daniels × CS 桥梁
全部 primary
姓名主要方向关系
Mohit TawarmalaniAllison & Nancy Schleicher Chair of Management; Executive Associate Dean of Faculty; Academic Director Krenicki Center for Business Analytics and MLDaniels primary, Krenicki Center Academic Director
Matthew LanhamBusiness analytics + ML applications; DanielsDaniels primary
Mohammad RahmanDaniels School Chair Professor; MIS Department; data analytics + AI for IT servicesDaniels MIS primary, Chair Professor
Yang WangDaniels Associate Dean for Curriculum; Clinical Associate Professor; analytics curriculumDaniels primary
Zaiyan WeiDavid A. Crow and Margaret J. Crow Rising Star Associate Professor; MIS + analyticsDaniels MIS primary

经三条标准筛查 Daniels School + Krenicki Center 关键 PI：5 位通过 AI 关键词匹配。Tawarmalani 全 3/3：Schleicher Chair + Krenicki Director + INFORMS Smith Prize 获奖项目 Academic Director。Rahman 是 Daniels Chair Professor。MSBAIM Top 10 是 Purdue 商科 AI 转型的旗舰。

Purdue 的核心优势是 公立旗舰大规模 + Daniels 商学院 MSBAIM 在线项目 USNews Top 10 + Online MSAI 双轨制（技术 + 政策）。最佳路径：(1) 在职 → Online MS in AI（AI & ML 或 AI Management & Policy major）；(2) 全日制 CS → MS in CS；(3) 数据科学 / 商科 → Daniels MSBAIM（residential 或 online）；(4) 数学 / 统计 → MS in Stat 或 MS in Applied Math。注意：Purdue 化学 / 物理 master-level AI 路径较弱, 主要靠 CS 选课。

来源：cs.purdue.edu · daniels.purdue.edu · purdue.edu/online · krenickicenter.purdue.edu
22

Harvard University

哈佛大学 · SEAS（John A. Paulson School of Engineering & Applied Sciences）· IACS（Inst for Applied Computational Science）· Kempner Institute（2022, $500M Zuckerberg/Chan 捐赠）· APCOMP 社区
USNews CS #16 (CS) · Top 5 (Stat) · Top 1-3 (HBS MBA) · Top 1 (Med)
①

AI program 核心专业课 & Listed Faculty

Harvard 的 AI 资源核心在 SEAS（工程学院, 2007 改名）+ FAS Statistics & CS + Kempner Institute（2022 创立, AI × 神经科学）。研究生层面验证：SM in Data Science（12 课 = 48 credits, 1.5 年, jointly led by CS + Stat）、SM in CSE（8 课, 1 年）, 两者共同构成 APCOMP 社区, 课程通过 AC 前缀 cross-list。Harvard 没有针对一般申请人的独立 MS-CS 课程项目——CS PhD 仅。Harvard PhD 学生可申请 Secondary Field in Data Science 或 CSE。

CS 1810Machine Learning（前 CS 181）
CS 1820Algorithms
CS 1200Computational Linear Algebra
CS 1210Computational Probability
CS 1240Adv Programming for DS
CS 2810Adv Machine Learning
CS 2870Probabilistic Topic Models
CS 2880Robust ML
AC 209a / CS 109aData Science 1（Protopapas, Rader）
AC 209b / CS 109bData Science 2: Advanced Topics
AC 215Practical Data Science: MLOps
AC 221Critical Thinking in Data Science
AC 295Adv Topics: DL for NLP
AC 299rReading and Research
AC 298rSeminar / Project
AC 302Thesis (DS thesis option)
STAT 110Probability
STAT 210 / 211Probability I / II（grad）
AM 207Stochastic Methods for DS
AM 221Advanced Optimization

Listed Faculty（节选）：

Pavlos Protopapas Kevin Rader Cengiz Pehlevan David Parkes Yiling Chen Stratos Idreos Joseph Blitzstein Lucas Janson Mark Glickman Daniel Weinstock

Pavlos Protopapas 是 IACS Scientific Director, Kevin Rader 是 AC 209 主讲。Cengiz Pehlevan 在 SEAS Applied Math + Kempner Institute 双 appointment, 是 ML × 物理跨界代表。Daniel Weinstock 是 Director of Master's Education。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

Harvard SEAS master 入口非常少：只有 SM in Data Science（12 课）和 SM in CSE（8 课）。两者都通过 GSAS 颁发, 由 OMPP（Office of Master's and Professional Programs）housed。注意：Harvard 没有 MS in CS 项目针对一般申请人——CS PhD 仅。SM-DS 允许至多 6 门 100/1000-level FAS 课, 实际课程灵活度高。

B · 学位计算

Harvard PhD 学生可以申请 Secondary Field in Data Science或 Secondary Field in CSE——这是 Harvard 给已在校 PhD 的额外认证（不是独立 master）。SM-DS 学生可以 cross-register 到 MIT（U-level 课）。Kempner Institute 提供 fellowship 和研究空间, 与 SM-DS / CSE 学位互补。

来源：seas.harvard.edu/masters-data-science · seas.harvard.edu/masters-computational-science-and-engineering · seas.harvard.edu/applied-computation/graduate-programs · statistics.fas.harvard.edu
③

与 AI 交叉的硕士项目（6 领域）

Harvard × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
AM (Master of Arts) in Statistics（FAS Stat 系, 通常仅 PhD 中途获得）
SM in Data Science（SEAS, jointly led by CS + Stat faculty, 12 courses = 48 credits, 1.5 年）
Harvard 没有针对一般申请人的独立 Stat MS 项目
SM-DS = Stat + CS 联合
Stat Top 5
USNews
 强
SM-DS 由 Stat 主导, 必修含 ML约 70%
 强
Stat ↔ CS 双系合作明文（SM-DS 由两系联合）
课程重合详情
SM-DS 必修 = AC 209a/b + CS 1810 + STAT 110/210
全部 重合
课号课程类型
STAT 110Introduction to ProbabilitySM-DS 推荐先修
STAT 111Statistical InferenceSM-DS 推荐先修
STAT 210 / 211Probability I/II（grad theory）PhD 主修, master 可选
AC 209aData Science 1（Pavlos Protopapas, Kevin Rader）SM-DS 必修核心
AC 209bData Science 2: Advanced TopicsSM-DS 必修核心
CS 109a / AC 209aIntro to Data Science（cross-list）可替代
AC 215Practical Data Science（MLOps）SM-DS 选修
AC 221Critical Thinking in Data ScienceSM-DS 选修
AC 295Advanced Topics in DL for NLPSM-DS 选修
CS 1810Machine Learning（前 CS 181）SM-DS 必修可选

Harvard SM in Data Science是由 Computer Science 和 Statistics 两系教授联合学术领导（jointly led by CS + Stat faculty）的项目, 12 门 letter-graded 课（48 学分）, 1.5 年（3 学期）。允许至多 6 门是 100/1000-level SEAS/FAS 课或 U-level MIT 课, 可跨注册到 MIT。Harvard 没有像 Columbia DSI 那样的开放申请的 Stat MS 项目, 一般申请人的 stat 入口 = SM-DS。Sham Kakade 是 Kempner Institute Co-Director（与 Sabatini 神经学家共同创办）。Lucas Janson 2025.10 升任正教授, 同年获 COPSS Emerging Leader Award。

师资重合详情
AC 209 由 Pavlos Protopapas + Kevin Rader 教（IACS）
全部 joint primary
姓名主要方向关系
Lucas JansonProfessor of Statistics + Affiliate in CS; high-dim inference + statistical ML; 2025 COPSS Emerging Leader Award; NSF CAREER + Bernoulli Society New Researcher Award + ASA Junior Noether; co-created STAT 184 ML with KakadeFAS Statistics primary + CS affiliate
Sham KakadeKempner Institute Co-Director; McKay Professor of CS + Professor of Statistics; ML theory, RL, deep learning, NLP, roboticsCS + Statistics joint, Kempner Co-Director
Susan MurphyMacArthur Fellow + NAS + NAM member; Statistical RL Lab Director; ML for sequential decision making in mobile health; SEAS CS + FAS Stat + Radcliffe Alumnae ProfessorSEAS CS + FAS Stat + Radcliffe joint, MacArthur/NAS/NAM
Tracy KeHigh-dim statistics, ML, social network analysis, text mining, bioinformatics, statistical genetics; chiseling subgroup selection (interactive ML)FAS Statistics primary
Pavlos ProtopapasIACS Scientific Director; ML × astrophysics; AC 209 lead instructor (Data Science 1)SEAS / IACS Scientific Director
Kevin RaderStatistics; AC 209 co-lead instructor; SM-DS curriculum architectSEAS Statistics primary
Joseph BlitzsteinStatistics; STAT 110 (Intro to Probability) lead; statistical foundationsFAS Statistics primary
Edoardo AiroldiNetwork statistics, ML for social networks; Airoldi Lab for Applied Stat Methodology & DS (formerly Harvard, now also BU)FAS Statistics primary (历史)
Mark GlickmanStatistics + ML; rating systems; Glicko algorithmFAS Statistics primary

经三条标准筛查 Harvard FAS Stat + SEAS CS-Stat 跨系：9 位通过 AI 关键词匹配。Janson 全 3/3：FAS Stat primary + COPSS 2025 + NSF CAREER + 与 Kakade 合教 STAT 184 ML。Kakade 是 Kempner Co-Director + 双系共聘。Murphy 是 MacArthur + NAS + NAM 三重荣誉, RL for health 主线。
Math
数学
AM in Mathematics（FAS Math, 通常 PhD 中途获得）
SM in Computational Science & Engineering（SEAS, 8 门课, 1 年）
无独立 Math MS for 一般申请人
SM-CSE 是数学背景出口
Math Top 5
USNews
 中
SM-CSE 含 ML elective约 50%
 中
SEAS APCOMP 社区跨系
课程重合详情
SM-CSE 8 门课 + ML elective 可选
全部 重合 elective
课号课程类型
AM 21AMathematical Methods for Sciences I基础
AM 22ALinear Algebra and Applications基础
AM 22BDifferential Equations基础
AM 106Applied AlgebraSM-CSE 可选
AM 115Mathematical ModelingSM-CSE 可选
AM 120Applicable Linear AlgebraSM-CSE 可选
AM 207Stochastic Methods for DSSM-CSE 核心
CS 1810Machine Learning可选
AC 215Practical DS, MLOps可选

Harvard SM in Computational Science & Engineering是 1 年 8 门课的项目, 由 SEAS 工程学院授予。CSE 与 Data Science 共同构成 Applied Computation (APCOMP) 社区, 共享课程基础设施（AC 前缀课程）。CSE 适合数学/物理/工程背景转 ML/Sci ML 的学生。Kempner Institute（2022 创立, $500M Zuckerberg/Chan 捐赠）把 SEAS Applied Math + CS + Statistics + Neuroscience 跨系连接, ML foundations group 由 Pehlevan、Janson、Kakade、Barak、Alvarez-Melis 等领衔。

师资重合详情
APCOMP / IACS 跨系
全部 joint
姓名主要方向关系
Cengiz PehlevanSEAS Applied Mathematics + Kempner Institute faculty; theoretical ML, neural network theory, computational neuroscience; ML × physics 桥梁SEAS Applied Math + Kempner Institute
David Alvarez-MelisAssistant Professor of CS + Kempner Associate Faculty; AI in data-scarce/dynamic/multi-modal environments; optimal transport theoryCS + Kempner Associate Faculty
Sham KakadeMcKay Professor of CS + Statistics; Kempner Institute Co-Director; ML theory + applied math foundationsCS + Stat + Kempner Co-Director
Boaz BarakGordon McKay Professor of CS; Kempner Institute Associate Faculty; theoretical CS + ML foundationsCS primary + Kempner Associate Faculty

经三条标准筛查 Harvard SEAS Applied Math + Kempner ML foundations：4 位通过 AI 关键词匹配。Pehlevan 全 3/3：SEAS Applied Math + Kempner core faculty + theoretical ML 主线（neural network theory）。Alvarez-Melis 是 CS + Kempner Associate Faculty + optimal transport AI。
Bio
生物
Harvard Medical School + School of Public Health 多 master 项目
BIG（Bioinformatics & Integrative Genomics）PhD
Harvard Chan School of Public Health 提供 MS in Health Data Science（HDS）
HDS + 多个 Med 项目
Med School Top 1
USNews
 强
HDS 含明确 ML 主线约 65%
 强
HMS / SPH × SEAS 多系共聘
课程重合详情
HDS / BIG 与 SEAS ML 课重合
全部 重合 elective
课号课程类型
BST 261Data Science II: Advanced ML（HDS 核心）HDS 必修
BST 232 / 234Methods I/II（HDS 必修）HDS 必修
BST 230Probability for BiostatisticsHDS 必修
BST 220Linear Models for BiostatisticsHDS 必修
CS 1810Machine Learning（SEAS）HDS 学生可选
AC 209aData Science 1HDS 学生可选

Harvard 的 "AI×Bio" 路径多通过 Harvard T.H. Chan School of Public Health 的 MS in Health Data Science (HDS)——Biostat 系运行, 含明确 ML 主线（BST 261）。Harvard Medical School DBMI（Department of Biomedical Informatics）是 AI for medicine 的旗舰——Founding Chair Isaac Kohane 是 NEJM AI 创刊主编（NEJM Group 旗下）, "the mission of DBMI is to advance the field of artificial intelligence toward improving our understanding of disease"。DBMI 的 AIM Track（AI in Medicine）由 Kohane + Patel + Manrai + Zitnik 联合教授。多人是 Kempner Institute Associate Faculty, 形成 HMS × Kempner 跨学院 AI 网络。

师资重合详情
HMS × CS 多位共聘（如 Gehlenborg）
全部 joint primary
姓名主要方向关系
Isaac KohaneFounding Chair of DBMI; Marion V. Nelson Professor of Biomedical Informatics; NEJM AI inaugural Editor-in-Chief; Boston Children's Hospital; precision medicine + AI for diseaseHMS DBMI Chair
Marinka ZitnikAssociate Professor DBMI + Kempner Associate Faculty + Broad Institute Associate Member + Harvard DSI Affiliated; founded Therapeutics Data Commons + lead International AI4Science; ML for biomedical discovery, graph neural networks, drug designHMS DBMI + Kempner + Broad + HDSI
Pranav RajpurkarAssistant Professor DBMI; medical AI + radiology + clinical NLP; NEJM AI editorial boardHMS DBMI primary
Andrew BeamHMS DBMI; NEJM AI Deputy Editor; biomedical ML + clinical AIHMS DBMI primary
Arjun ManraiHMS DBMI; NEJM AI Deputy Editor; AI for medicine + clinical informaticsHMS DBMI primary
Chirag PatelHMS DBMI; AI for medicine + environmental health + EHR MLHMS DBMI primary
Peter ParkHMS DBMI; computational genomics + MLHMS DBMI primary
Heng LiHMS DBMI; bioinformatics algorithms + sequencing dataHMS DBMI primary
Kun-Hsing YuHMS DBMI; ML for pathology + computational biologyHMS DBMI primary
Susan MurphySEAS CS + Stat + Kempner Associate Faculty; RL for mobile health interventionsSEAS CS + Stat + Kempner
Curtis HuttenhowerHarvard Chan SPH; microbiome ML + computational biologyHSPH Biostat primary
John QuackenbushHarvard Chan SPH; bioinformatics + computational genomicsHSPH Biostat primary
Nils GehlenborgHMS DBMI; biomedical visualization + MLHMS DBMI + SEAS CS affiliated

经三条标准筛查 HMS DBMI + HSPH Biostat + Kempner 跨系：13 位通过 AI 关键词匹配。Kohane 全 3/3：DBMI Chair + Marion V. Nelson Professor + NEJM AI 创刊主编。Zitnik 是 DBMI + Kempner + Broad + HDSI 四系/中心交叉, NeurIPS/Nature Comm/PNAS 论文 + Therapeutics Data Commons 创始。
Chem
化学
AM in Chemistry（FAS, 通常仅 PhD 中途获得）
无 AI×Chem 专门 master
Chem MS 罕见
Chem Top 5
USNews
 弱
Chem MS 罕见, 必修无 ML< 25%
 弱
无系统交叉
课程重合详情
—
全部
课号课程类型

Harvard 没有针对一般申请人的 Chem MS。系内 ML × Chem 主线主要由 Boris Kozinsky 的 MIR (Materials Intelligence Research) group 承担（mir.g.harvard.edu）, 跨 SEAS MSE + Chemistry + Bosch Research 三方。Aspuru-Guzik 已 2018 年转 Toronto。但 Kozinsky 团队 2018 在 Harvard 建立, 是当前 ML × materials/chemistry 的核心。

师资重合详情
—
全部 joint primary
姓名主要方向关系
Boris KozinskyGordon McKay Professor of Materials Science and Mechanical Engineering at SEAS, Professor of Chemistry and Chemical Biology, Principal Scientist at Bosch Research; MIR (Materials Intelligence Research) group Director; ML-accelerated atomistic + electronic structure computations for catalysts, ferroelectrics, batteries, polymers, 2D materialsSEAS MSE/Mech Eng + Chemistry + Bosch
Lee groupTheoretical/computational methods for chemical phenomena, ML-augmented quantum chemistryChemistry primary, Theory

经三条标准筛查 Harvard Chem + 跨系 ML × Chem：2 位通过 AI 关键词匹配。Kozinsky 全 3/3：Chemistry + SEAS MSE/Mech Eng + Bosch + MIR group Director + 主页明文 "machine learning techniques accelerate atomistic computation"。
Phys
物理
AM in Physics（FAS, 通常仅 PhD 中途获得）
SM-CSE（适合物理 → Sci ML）
无独立 AI×Phys master
CSE 是物理常见出口
Phys Top 1-3
USNews
 中
SM-CSE 直接对接物理约 45%
 强
Pehlevan 等 SEAS Applied Math + Kempner 跨系
课程重合详情
SM-CSE + Kempner Institute 资源
全部 重合 elective
课号课程类型
SM-CSE 8 门核心—SM-CSE 必修
AM 207Stochastic Methods for DSSM-CSE 核心
CS 1810Machine Learning可选
AC 215Practical DS, MLOps可选

Kempner Institute for the Study of Natural and Artificial Intelligence是 Harvard 2022 年由 Mark Zuckerberg / Priscilla Chan $500M 捐赠创立的 AI × 神经科学跨学科研究所。Pehlevan 等理论物理 + ML 学者提供了"物理 → ML"的桥梁。SM-CSE 是物理学生最直接的 master 出口。IAIFI (NSF AI Institute for Artificial Intelligence and Fundamental Interactions, $20M)是 Harvard + MIT + Tufts + Northeastern 联合的 AI × physics 研究院, Cora Dvorkin 任 Harvard 代表。

师资重合详情
Cengiz Pehlevan 等 ML 理论物理学家
全部 joint primary
姓名主要方向关系
Cengiz PehlevanSEAS Applied Mathematics + Kempner Institute; theoretical ML, neural network theory; physics × ML 桥梁SEAS Applied Math + Kempner Institute
Cora DvorkinProfessor of Physics; theoretical cosmologist; Harvard Representative on IAIFI (NSF AI Institute) Board; 2019 DOE Early Career Award; ML for dark matter detection in gravitational lensing systems; CMB + LSS data ML; CMB-S4 collaborationPhysics primary, IAIFI Board
Matthew SchwartzProfessor of Physics; co-leader Harvard side of IAIFI (NSF AI Institute); ML for high-energy physics + theoretical particle physicsPhysics primary, IAIFI
Carlos Argüelles-DelgadoPhysics & Laboratory for Particle Physics and Cosmology; quantum computing for HEP data analysis + MLPhysics primary

经三条标准筛查 Harvard Physics + Kempner + IAIFI：4 位通过 AI 关键词匹配。Dvorkin 全 3/3：Physics primary + IAIFI Board + DOE Early Career + ML for dark matter 主线 + Snowmass 2021 ML × Cosmology white paper 主笔。Pehlevan 是 Applied Math + Kempner ML 理论核心。
Biz
商科
HBS MBA（Top 1-3）
HKS（Kennedy School）MPP/MPA
HBS 与 SEAS 之间有交叉项目
HBS MBA 顶级
HBS MBA Top 1-3
USNews
 中
HBS MBA 含 BA elective约 50%
 中
HBS × SEAS 部分合作
课程重合详情
HBS BA 课 + SEAS ML elective
全部 重合 elective
课号课程类型
TOM (Tech & Operations Mgmt)—HBS MBA elective 群
Data Science for Business—HBS MBA elective
CS 1810Machine LearningHBS 学生可选

HBS MBA 是 USNews MBA Top 1-3。Harvard Digital Data Design Institute (D^3) 由 Karim Lakhani 任 Chair, 是 HBS 的 AI/digital 旗舰研究中心。Lakhani + Iansiti 合著的《Competing in the Age of AI》是 HBR Press 2020 年代表作。HBS × SEAS 之间有交叉, 但没有像 Northwestern MLDS-Kellogg 那样的正式 dual master。HBS Online 与 SEAS 合开 "AI Business Essentials" 4 周课。

师资重合详情
HBS × CS 共聘有限
全部 primary
姓名主要方向关系
Karim R. LakhaniDorothy and Michael Hintze Professor of Business Administration; Chair, Harvard Digital Data Design Institute (D^3); co-author "Competing in the Age of AI"; founder/co-director Lab for Innovation Science at Harvard; PI NASA Tournament LabHBS primary, D^3 Institute Chair
Marco IansitiDavid Sarnoff Professor of Business Administration; Head, Technology and Operations Management Unit + Digital Initiative at HBS; co-author "Competing in the Age of AI"; co-creator HBS Online AI Business EssentialsHBS primary, TOM Unit Head
Iavor BojinovHBS Assistant Professor of Business Administration; ML for causal inference + experimentation; co-author JPMC Generative AI caseHBS primary
Feng ZhuHBS Professor; platforms, data network effects, ML for online platformsHBS primary

经三条标准筛查 HBS AI 主线：4 位通过 AI 关键词匹配。Lakhani 全 3/3：HBS primary + Hintze Endowed Professor + D^3 Institute Chair + HBR cover 文章。Iansiti 是 David Sarnoff Endowed Professor + Digital Initiative Head + HBS Online AI 课主讲。

Harvard 的核心优势是 品牌 + 跨学院全面深度（SEAS + FAS Stat + Med + HBS + Kennedy）+ Kempner Institute（$500M 投入 AI ×神经科学）+ 与 MIT 跨注册。最佳路径：(1) 数据科学 / 一般 AI → SM-DS（12 课 48 学分, jointly led CS + Stat）；(2) 数学 / 物理 / 工程 → SM-CSE（8 课, 1 年, 灵活）；(3) Harvard PhD 在读 → Secondary Field in DS；(4) 生医 → Harvard Chan School MS-HDS；(5) 商科 → HBS MBA。注意：Harvard 没有针对一般申请人的 MS-CS 项目, 化学 / 物理 master-level AI 路径较弱（必须经 SM-CSE）。

来源：seas.harvard.edu · statistics.fas.harvard.edu · hsph.harvard.edu · hbs.edu · kempnerinstitute.harvard.edu
23

New York University

纽约大学 · CDS（Center for Data Science, Yann LeCun 创办）· Courant Institute（Applied Math Top 1）· Tandon Engineering · Stern MSBA Top 3
USNews CS #33 (CS) · Top 1 (Applied Math) · Top 10 (Stern MBA) · Top 3 (Stern MSBA)
①

AI program 核心专业课 & Listed Faculty

NYU 的 AI 资源核心横跨 CDS（Center for Data Science）（Yann LeCun 2014 年创办）+ Courant Institute of Mathematical Sciences（含 Math + CS, USNews Applied Math Top 1）+ Tandon School of Engineering（前身 Polytechnic, 在 Brooklyn）+ Stern School of Business。研究生层面验证：CDS MSDS（36 学分, 含 4 tracks）、Courant CS MS、Courant MathFin MS、Courant SciComp MS、Tandon Computer Engineering MS、Stern MSBA、Stern MBA。

DS-GA 1001Introduction to Data Science
DS-GA 1002Probability and Statistics for DS
DS-GA 1003Machine Learning（grad core）
DS-GA 1004Big Data
DS-GA 1005Inference and Representation
DS-GA 1008Deep Learning（LeCun, Canziani）
DS-GA 1011Fundamentals of NLP
DS-GA 1012Large Language Models
DS-GA 1015Text as Data
DS-GA 1018Probabilistic Time Series
CSCI-GA 2565Machine Learning（Courant）
CSCI-GA 2566Foundations of Machine Learning
CSCI-GA 2271Computer Vision
CSCI-GA 2590Natural Language Processing
CSCI-GA 3033Special Topics（含 GenAI 等按年）
CSCI-GA 2572Deep Learning（cross-list with DS-GA 1008）

Listed Faculty（节选）：

Yann LeCun Kyunghyun Cho Sam Bowman He He Andrew Wilson Joan Bruna Mehryar Mohri Lerrel Pinto Saining Xie Rajesh Ranganath Tal Linzen Krzysztof Geras Foster Provost Andrew Owens

Yann LeCun 兼任 Meta VP / Chief AI Scientist。Kyunghyun Cho 同时在 Genentech 任研究科学家。Sam Bowman 同时在 Anthropic（NYU 学术休假, 公开）。这种"NYU + 大厂双 appointment"是 NYU 师资的特色。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

CDS MSDS 36 学分项目, 必修 DS-GA 1001-1005, 含 1 个 Data Science Elective（如 DS-GA 1008 Deep Learning, 1011 NLP, 1012 LLMs 等）。MSDS 提供 4 个 Tracks（Industry Concentration, Big Data, NLP, Biomedical Informatics）。

B · 学位计算

CDS / Courant / Tandon / Stern 之间课程互通, MSDS 学生可以将 Courant CS（CSCI-GA）和 Stern 课程作为 elective（需要 advisor 批准）。Courant CS PhD 与 CDS PhD 学生共享 DS-GA 系列（DS-GA 1008 = CSCI-GA 2572 cross-list）。

来源：cds.nyu.edu/masters-in-data-science-curriculum · bulletins.nyu.edu/courses/ds_ga · cs.nyu.edu · stern.nyu.edu
③

与 AI 交叉的硕士项目（6 领域）

NYU × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
Yann LeCun 创办的 Center for Data Science (CDS) · MSDS（Master's in Data Science, 36 学分）
注：NYU Stern 商学院的 MS in Statistics（独立项目）
NYU 没有传统的 GSAS Statistics 系
CDS MSDS = 旗舰
Stat Top 25
估算
 强
MSDS 必修含 ML/DL/NLP约 70%
 强
LeCun 等 CDS + Courant CS 双系
课程重合详情
MSDS 必修 = DS-GA 1001-1005 + 1008
全部 重合
课号课程类型
DS-GA 1001Introduction to Data ScienceMSDS 必修核心
DS-GA 1002Probability and Stats for Data ScienceMSDS 必修核心（可 waive）
DS-GA 1003Machine Learning（He He 等主讲）MSDS 必修核心
DS-GA 1004Big DataMSDS 必修核心
DS-GA 1005Inference and RepresentationMSDS 必修核心
DS-GA 1008Deep Learning（Yann LeCun + Alfredo Canziani, cross-list CSCI-GA 2572）MSDS 选修旗舰
DS-GA 1011Fundamentals of NLPNLP track
DS-GA 1012Large Language Models: Evaluation & ApplicationsNLP track 旗舰
DS-GA 1015Text as DataNLP track
DS-GA 1018Probabilistic Time Series Analysiselective

NYU CDS（Center for Data Science）由 Yann LeCun 在 2012 年创办（founding director, 2012-2014）, 是美国最早的独立数据科学学院之一。MSDS（Master's in Data Science, 36 学分）是旗舰 master 项目, DS-GA 1001-1005 + 1008 系列是核心课。2025 年 11 月 LeCun 离开 Meta 创办 AMI Labs（Advanced Machine Intelligence Labs）, 仍保留 NYU 全职教授职位。CILVR Lab（Computational Intelligence, Learning, Vision, Robotics）由 LeCun + Cho + Fergus + Pinto 等领衔。Global AI Frontier Lab（2024 NYU + Korea Ministry of Science）是新的旗舰国际合作项目。

师资重合详情
CDS faculty 多在 Courant CS + Meta
全部 joint
姓名主要方向关系
Yann LeCun2018 Turing Award; NAS + NAE + French Académie des Sciences; Jacob T. Schwartz Chaired Professor at Courant + Silver Professor + CDS Founding Director (2012); 2025 Queen Elizabeth Prize for Engineering; 2025 NYAS Trailblazer Award; left Meta Nov 2025 to found AMI Labs (Advanced Machine Intelligence)CDS + Courant CS, Turing/NAS/NAE/French Academy
Kyunghyun ChoAssociate Professor of CS + Data Science; NeurIPS 2025 Keynote ("Problem Finding in AI Research"); foundation models, NLP, ICLR co-founder; also Genentech Senior Director (Prescient Design); 2023 Business Insider 30 Leaders Under 40 HealthcareCDS + Courant CS + Genentech
He HeAssistant Professor of CS + Data Science; 2024 Samsung AI Researcher of the Year; NeurIPS 2024 Best Paper Award (PRISM Alignment Dataset); NLP, ML, imitation learningCourant CS + CDS, Samsung 2024
Andrew Gordon WilsonAssociate Professor of CS + Data Science; NSF CAREER 2022; ICML 2022 Best Paper Award; Bayesian deep learning, Gaussian processes, ML foundationsCDS + Courant CS, ICML Best Paper 2022
Rajesh RanganathAssistant Professor of CS + Data Science; probabilistic inference, ML for healthcare; AISTATS 2023 Notable Paper Award + NSF CAREER 2022Courant CS + CDS, AISTATS Notable 2023
Mengye RenAssistant Professor of CS + Data Science; Agentic Learning AI Lab Director; continual learning, meta-learning, computer vision; previously Google Brain visiting researcher (Hinton group)Courant CS + CDS
Joan BrunaProfessor of CS + Data Science + Math (affiliated); ICLR 2024 Test-of-Time Award runner-up ("Intriguing Properties of Neural Networks", 2014); ML theory + geometric deep learningCourant Math + CS + CDS
Carlos Fernandez-GrandaAssociate Professor of Mathematics + Data Science; mathematical foundations of ML, signal processing, ML for medical imaging; author of "Probability and Statistics for Data Sciences"Courant Math + CDS
Tal LinzenAssociate Professor of Linguistics + Data Science; computational linguistics + NLP + cognitive scienceLinguistics + CDS
Brenden LakeAssociate Professor of Psychology + Data Science; cognitive science + ML; few-shot learning, computational models of cognitionPsychology + CDS
Julia KempeProfessor of CS + Math + Data Science; quantum computing, ML theoryCourant Math/CS + CDS
Mehryar MohriProfessor of CS + Math + Data Science; ML foundations + algorithm theory; "Foundations of Machine Learning" textbook authorCourant CS + CDS + Google
Eero SimoncelliProfessor of Neural Science + Math + Psych + Data Science; HHMI Investigator; visual perception + computational neuroscienceNeural Science + CDS, HHMI
Cristina SavinAssistant Professor of Neural Science + Data Science; learning, memory, neural circuits, probabilistic computationNeural Science + CDS
Brian McFeeAssistant Professor of Music Technology + Data Science; ML for audio + music information retrievalMusic Tech + CDS

经三条标准筛查 NYU CDS + Courant CS：15 位通过 AI 关键词匹配。LeCun 全 3/3：CDS Founding Director + JT Schwartz Chaired Professor + Turing/NAS/NAE/French Academy 四重院士。Cho 是 NeurIPS 2025 Keynote。He He 是 2024 Samsung AI 年度研究员 + NeurIPS 2024 Best Paper。Wilson 是 ICML 2022 Best Paper。
Math
数学
Courant Institute · MS in Mathematics
Courant · MS in Mathematics in Finance（MathFin, 顶级 quant）
Courant · MS in Scientific Computing
Courant 三个 master
Math Top 1（Applied）
USNews
 强
Courant Math 是 Applied Math Top 1 + ML 强约 65%
 强
Courant Math + CS + CDS 三系联动
课程重合详情
Courant MathFin / SciComp + CDS ML 课
全部 重合 elective 独有
课号课程类型
MATH-GA 2470Ordinary Differential EquationsMath 核心
MATH-GA 2010Numerical Methods ISciComp 核心
MATH-GA 2706PDEs for FinanceMathFin elective
MATH-GA 2752Active Portfolio ManagementMathFin elective
MATH-GA 2563Risk and Portfolio Mgmt with EconometricsMathFin 核心
MATH-GA 2070Numerical Methods IISciComp 核心
CSCI-GA 2565Machine Learning跨系
DS-GA 1003Machine Learning跨系

Courant Institute 是美国 Applied Math 长期 USNews #1。MS in Mathematics in Finance（MathFin）是顶级 quant 项目, MS in Scientific Computing（SciComp）是数学/物理转 Sci ML 的桥梁。Joan Bruna（Courant Math + CS + CDS 三系交叉）是 Math×ML 代表。Mehryar Mohri 的"Foundations of Machine Learning"是 ML 理论经典教科书。

师资重合详情
Joan Bruna 等 Courant + CDS
全部 joint primary
姓名主要方向关系
Joan BrunaProfessor of CS + Data Science + Math (affiliated); ML theory + geometric deep learning; ICLR 2024 Test-of-Time runner-upCourant Math + CS + CDS
Carlos Fernandez-GrandaAssociate Professor of Mathematics + Data Science; mathematical foundations of ML, signal processingCourant Math + CDS
Mathieu LaurièreAssistant Professor (Shanghai); mean field games + RL + multi-agent learningMath/Shanghai
Shuyang LingAssistant Professor; mathematical optimization + ML; data science applicationsMath/Shanghai
Esteban TabakProfessor of Mathematics; probability/optimization, density estimation via MLCourant Math primary
Oded RegevProfessor of CS; theoretical CS, lattice cryptography (Regev cryptosystem 基础); ML adjacencyCourant CS primary
Mehryar MohriProfessor of CS + Math + Data Science; ML foundations + algorithm theoryCourant CS + CDS + Google
Julia KempeProfessor of CS + Math + Data Science; quantum computing + ML theoryCourant Math/CS + CDS

经三条标准筛查 Courant Math + Math×CDS 跨系：8 位通过 AI 关键词匹配。Bruna 全 3/3：Courant Math + CS + CDS 三系 + ICLR 2024 Test-of-Time runner-up + 几何深度学习开创者。Mohri 是 ML 理论权威 + Google 研究员。
Bio
生物
CDS MSDS Biomedical Informatics Track
NYU School of Medicine 提供 MS in Biostatistics 等
NYU Tandon 部分项目支持 Bio 应用
CDS Biomed Track
Med School Top 5
USNews
 强
CDS Biomed Track 含 ML 主线约 60%
 强
CDS × NYU Med 合作
课程重合详情
CDS Biomed Track + DS-GA ML 课
全部 重合
课号课程类型
DS-GA 3001Special Topics（含 Biomedical AI 主题）Biomed track elective
DS-GA 1003Machine LearningMSDS 必修
DS-GA 1011Fundamentals of NLP适用于生医 NLP

NYU CDS MSDS 提供 Biomedical Informatics Track。NYU Grossman School of Medicine 的 MS in Biomedical Informatics（Vilcek Institute）是 34 学分硕士项目, 必修包含 Machine Learning (BMIN-GA 1004) + Deep Learning for Biomedical Data (BMSC-GA 4439)。fastMRI 项目（NYU Langone + Facebook AI / Meta）是 AI×MRI 旗舰开源研究。CAI2R（Center for Advanced Imaging Innovation and Research, NIBIB-NIH 资助）由 Daniel Sodickson 领衔。

师资重合详情
CDS × NYU Med 多位共聘
全部 joint primary
姓名主要方向关系
Rajesh RanganathAssistant Professor of CS + Data Science; ML for healthcare, probabilistic inference; AISTATS 2023 Notable Paper + NSF CAREER 2022Courant CS + CDS
Krzysztof J. GerasAssistant Professor of Radiology + DBMI + CDS; medical imaging ML (mammography deep learning); NYU Grossman School of MedicineGrossman + CDS
Kyunghyun ChoCDS + Courant CS + Genentech Senior Director (Prescient Design); drug discovery ML, antibody design; ICLR 2024 outstanding paper (Protein Discovery with Discrete Walk-Jump Sampling)CDS + Genentech
Yindalon AphinyanaphongsAssociate Professor of Population Health + Medicine; AI for healthcare, clinical decision support, EHR MLGrossman primary
Sumit ChopraAssociate Professor of Radiology + CS; ML for medical imaging, MRI reconstruction; CDS affiliatedGrossman Radiology + CS
Nargez RazavianAssistant Professor of Population Health + Medicine; clinical ML, predictive models from EHRGrossman primary
Aristotelis TsirigosProfessor of Pathology + Medicine; computational pathology, ML for cancer genomics; Vilcek InstituteGrossman Pathology primary
Daniel SodicksonProfessor of Radiology + Tandon ECE; AI for MRI, fastMRI co-leader (with Facebook AI); Center for Advanced Imaging Innovation and Research (CAI2R) DirectorGrossman Radiology + Tandon ECE
Florian KnollAssociate Professor of Radiology; MRI ML reconstruction, fastMRIGrossman Radiology primary
Yvonne LuiProfessor of Radiology; neuroimaging AI, traumatic brain injury imagingGrossman Radiology primary
Rumi ChunaraAssociate Professor of Biostatistics + CS (Tandon); AI for public health, fairness in ML; School of Global Public Health + Tandon CSEGPH + Tandon CS
Nadav BrandesAssistant Professor at Grossman; Brandes Lab — genomic language models for disease mutations + early diagnosisGrossman primary
Souptik BaruaAssistant Professor of Medicine; ML/AI for digital health, wearables, mHealthGrossman primary
Carlos Fernandez-GrandaCourant Math + CDS; ML for medical imagingCourant Math + CDS
Saul BleckerAssociate Professor of Population Health; clinical informatics + MLGrossman primary
Leora HorwitzProfessor of Population Health + Medicine; health services research + ML; Director Center for Healthcare Innovation and Delivery ScienceGrossman primary

经三条标准筛查 Grossman + Tandon Bio + CDS 跨系：16 位通过 AI 关键词匹配。Sodickson 全 3/3：Grossman Radiology + Tandon ECE + CAI2R Director + fastMRI co-leader。Tsirigos 是 Vilcek Institute Pathology + 计算肿瘤基因组学。
Chem
化学
MS in Chemistry（少见为终端学位）
无 AI×Chem 专门 master
Chem MS 罕见
Chem Top 30
估算
 弱
Chem MS 无 ML< 25%
 弱
无系统交叉
课程重合详情
—
全部
课号课程类型

基于"无官方依据则不列"原则, 此格仅做空白说明。

师资重合详情
—
全部
姓名主要方向关系

Chem 系无明确 ML 主线 faculty 路径。
Phys
物理
MS in Physics（PhD 中途获得）
Courant SciComp MS（物理 → Sci ML 桥梁）
SciComp 是物理出口
Phys Top 25
估算
 中
SciComp 直接对接物理约 40%
 中
物理 ↔ Courant 部分共聘
课程重合详情
SciComp + CDS ML 课
全部 重合 elective
课号课程类型
MATH-GA 2010Numerical Methods ISciComp 核心
MATH-GA 2070Numerical Methods IISciComp 核心
CSCI-GA 2565Machine Learning需 override
DS-GA 1003Machine Learning可选

NYU Physics PhD 主导。"AI×Physics" 主要通过 Courant SciComp MS 或 CDS 选课实现。Courant Math 的 ML × physical sciences 主线（Bruna, Fernandez-Granda, Tabak）是物理转 ML 的桥梁。Center for Cosmology and Particle Physics (CCPP) 在 NYU Physics, 也有 ML × cosmology 方向, 但 Master-level 路径多通过 SciComp。

师资重合详情
物理 × Courant Math 跨系
全部 joint primary
姓名主要方向关系
Joan BrunaCourant Math + CS + CDS; ML theory + geometric deep learning, applications to physical sciencesCourant Math + CS + CDS
Carlos Fernandez-GrandaCourant Math + CDS; signal processing, ML for scientific dataCourant Math + CDS
Esteban TabakCourant Math; ML for density estimation + applications in fluid dynamics + climateCourant Math primary
Eero SimoncelliNeural Science + CDS + HHMI; computational neuroscience, image processing, ML × physics of perceptionNeural Science + CDS, HHMI

Physics 系内独立 ML 主线 master-level faculty 路径在公开页面较弱。Courant Math + CDS 跨系是物理 → ML 的主要桥梁。本表只列出 4 位直接 ML × physical sciences 跨界的 PI。
Biz
商科
Stern School of Business MBA（Top 10）
Stern MS in Business Analytics（顶级, 1 年制 STEM, 国际声誉）
Stern MS in Quantitative Management
Stern MSBA Top 3
Stern MBA Top 10
USNews
 强
Stern MSBA 含完整 ML 训练约 70%
 强
Stern × CDS / Courant 部分合作
课程重合详情
Stern MSBA 必修 + CDS elective
全部 重合 elective
课号课程类型
OPMG-GB 2350Operations ManagementStern 核心
STAT-GB 2301Statistics for Business（基础）Stern 基础
TECH-GB 2336Data Mining for Business AnalyticsStern MSBA 核心
TECH-GB 2350Adv Topics: ML for BusinessStern MSBA elective
DS-GA 1003Machine LearningStern 学生需 override

Stern MSBAi (MS in Business Analytics and AI, 1 年 STEM)是 USNews / FT 国际排名 Top 3 的商业分析 master, 2024 年由 MSBA 改名为 MSBAi（加入 AI），由 Anindya Ghose 任 Academic Director（since 2017）。Stern MBA 是 USNews Top 10。Foster Provost 是 NYU CDS 前 interim Director + Fubon Center Director + 2020 ACM SIGKDD Test of Time Award 得主, "Data Science for Business" 是商学院经典教科书。NYU Stern 是 2013 年第一所推出 MSBA 的 Top 商学院（10+ 年历史）。

师资重合详情
Stern × CDS faculty 部分共聘
全部 joint primary
姓名主要方向关系
Anindya GhoseHeinz Riehl Chair Professor of Technology, Operations and Statistics; MSBAi Academic Director (since 2017); 115+ papers on data privacy + AI; author "TAP: Unlocking the Mobile Economy"; consulted Google + MicrosoftStern primary, MSBAi Director
Foster ProvostIra Rennert Professor of Entrepreneurship and Information Systems; Director, Fubon Center for Data Analytics & AI; Professor of Data Science at NYU; former interim Director NYU CDS; 2020 ACM SIGKDD Test of Time Award; "Data Science for Business" textbook author; former Editor-in-Chief Machine Learning journalStern + CDS, ACM SIGKDD Test of Time
Arun SundararajanHarold Price Professor of Entrepreneurship + Professor of Tech Operations and Statistics; 2015 Thinkers50 Top Management Professor; AI in business platforms, sharing economy; Bloomberg + NYT AI commentary regularStern primary, Thinkers50 2015
Xi ChenAssociate Professor of Technology Operations and Statistics; 2021 Poets&Quants 40-Under-40 Best MBA Professor; ML + statistical learning theory + crowdsourcingStern primary, P&Q 40-Under-40
J.P. EggersVice Dean of MBA & Graduate Programs at Stern; Daniel and Diana Sandberg Professor; technology management + organizational learning + AI strategyStern Vice Dean MBA primary
Vasant DharProfessor of Technology + Information Systems; founding Director of NYU Stern's PhD program in Information Systems; ML + finance + algorithmic trading; CDS facultyStern + CDS

经三条标准筛查 Stern + CDS 跨系：6 位通过 AI 关键词匹配。Provost 全 3/3：Stern Ira Rennert Endowed Professor + Fubon Center Director + ACM SIGKDD Test of Time + Data Science for Business 教科书。Ghose 全 3/3：Heinz Riehl Endowed Chair + MSBAi Director + 115+ AI papers。

NYU 的核心优势是 纽约市地理位置 + LeCun 创办的 CDS + Courant Applied Math Top 1 + 师资双 appointment（Meta/Anthropic/Genentech 等）。最佳路径：(1) 数据科学核心 → CDS MSDS（36 学分, 4 tracks）；(2) 数学 / 量化 → Courant MathFin（顶级 quant）或 SciComp；(3) 商科 → Stern MSBA（Top 3 国际）；(4) CS 工程 → Courant CS MS。注意：NYU 化学 / 物理 master-level AI 路径较弱, 主要靠 Courant + CDS 选课。

来源：cds.nyu.edu · cs.nyu.edu · math.nyu.edu · stern.nyu.edu · tandon.nyu.edu
24

Duke University

杜克大学 · MIDS（Master in Interdisciplinary Data Science, SSRI 主导）· AIPI（MEng in AI for Product Innovation, Pratt 2021 启动）· Fuqua MBA Top 10
USNews CS #22 (CS) · Top 5 (Stat) · Top 10 (Fuqua MBA, Med, Biostat)
①

AI program 核心专业课 & Listed Faculty

Duke 的 AI 资源横跨 Trinity College of Arts & Sciences 的 CS Department + Pratt School of Engineering（含 ECE、Biomedical Engineering）+ Fuqua School of Business。研究生层面验证：MIDS（42 学分, Social Science Research Institute 主导, 含 30 学分 IDS + 12 学分 elective）、AIPI MEng（Pratt, 2021 启动, 12/16 月 on-campus 或 24 月 online）、MS in CS、MS in Statistical Science、Fuqua MQM-BA 等。

COMPSCI 471Deep Learning
COMPSCI 526Data Science
COMPSCI 671Theory and Algorithms for Machine Learning
COMPSCI 670Computer Vision
COMPSCI 590.XSpecial Topics（按年含 LLMs / RL / GenAI 等）
STA 663Statistical Computing
STA 521Statistical Science Practice
IDS 705Principles of Machine Learning（MIDS 核心）
IDS 720Data Logic, Vis & Stats
AIPI 510Sourcing Data for Analytics
AIPI 520Modeling Process and Algorithms
AIPI 540Deep Learning Applications
AIPI 549Industry Capstone
AIPI 560Legal, Societal, & Ethical Issues in AI
AIPI 561Operationalizing AI（MLOps）
ECE 580 / 581Inference / Detection & Estimation
ECE 685DIntroduction to Deep Learning

Listed Faculty（节选）：

Cynthia Rudin Vincent Conitzer David Dunson Sayan Mukherjee Pankaj Agarwal Vahid Tarokh Lawrence Carin Carlo Tomasi Jon Reifschneider Surya Tokdar

Cynthia Rudin 是 Earl D. McLean, Jr. Professor（注：原 Lehrman Distinguished Professor 已更新）, CS + ECE + Stat + Biostat 4-system 联合 appointment, 是该校跨系 ML 代表。Vincent Conitzer 也兼任 CMU appointment。Lawrence Carin 是 ECE + DS。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

Duke 三条 master 入口：MIDS（42 学分, Social Science Research Institute 主导, 30 学分核心 IDS 课 + 12 学分 elective, 含 IDS 705 必修）、AIPI MEng（cohort-based, 12/16 月 on-campus 或 24 月 online, AIPI 510-561 系列）、MS in CS（30 学分, 在 CS 系下）。AIPI 是 Pratt School Engineering 直接颁授的 MEng（不是 MS）。

B · 学位计算

Duke 不同 master 之间课程互通：MIDS / MS-Stat / MS-CS 学生可以将 IDS 705 / COMPSCI 671 / STA 663 等算入 elective。AIPI 提供4+1 BSE+MEng（20% 学费 scholarship）+ MD+MEng AIPI dual degree——这是工程导向 AI master 中较罕见的医学双学位选项。

来源：datascience.duke.edu/academics/mids-courses · masters.pratt.duke.edu/aipi/degree · gradschool.duke.edu/academics/programs-degrees/master-interdisciplinary-data-science · stat.duke.edu · cs.duke.edu
③

与 AI 交叉的硕士项目（6 领域）

Duke × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MS in Statistical Science（Stat 系, 30 学分）
MIDS（Master in Interdisciplinary Data Science, SSRI 主导, 42 学分）
注：Duke Stat 系是美国最早成立的现代 Bayesian Stat 系之一
MS-Stat + MIDS
Stat Top 5
USNews
 强
MIDS 与 MS-Stat 课程互通约 70%
 强
Cynthia Rudin = CS+ECE+Stat+Biostat
课程重合详情
MS-Stat 必修与 CS ML 课重合
全部 重合 等价
课号课程类型
STA 521Statistical Science PracticeMS Stat 必修核心
STA 631Statistical InferenceMS Stat 必修核心
STA 633Bayesian MethodsMS Stat advanced
STA 663Statistical ComputingMS Stat 核心
STA 711Probability & Measure TheoryPhD-level
IDS 705Principles of Machine LearningMIDS 核心
IDS 720Data Logic, Visualization & StatisticsMIDS 核心
COMPSCI 671Theory and Algorithms for Machine Learning需 override

Duke Stat 系是现代 Bayesian Stats 学派的发源地之一（David Dunson 等）。Cynthia Rudin 是 5-system joint appointment（CS+ECE+Stat+Biostat+Math 联合）, 2022 AAAI Squirrel AI Award 得主（"AI 界的诺贝尔奖"）+ 2025 ACM Fellow + 2024 AAAS Fellow。MIDS 与 MS-Stat 之间课程可互通, IDS 705 是 ML 必修。Eric Laber 是 Duke Computing Initiative Co-Director。

师资重合详情
Stat × CS 多位共聘
全部 joint primary
姓名主要方向关系
Cynthia RudinGilbert, Louis, and Edward Lehrman Distinguished Professor; Professor of CS + ECE + Statistical Science + Biostatistics & Bioinformatics + Math (5-system joint); Director, Interpretable Machine Learning Lab; 2022 AAAI Squirrel AI Award ("Nobel Prize of AI"); 2025 ACM Fellow; 2024 AAAS Fellow; ASA + IMS + AAAI Fellow; "Stop Explaining Black Box ML Models" Nature Machine Intelligence (2019)CS + ECE + Stat + Biostat + Math, AAAI Squirrel AI 2022
David B. DunsonArts and Sciences Distinguished Professor of Statistical Science, Mathematics and Electrical & Computer Engineering; joint Editor of JRSS-B; Bayesian + statistical ML methodology for high-dim data; ecology/biodiversity, neuroscience, environmental health, genomics applicationsStat + Math + ECE
Rebecca SteortsAssociate Professor of Statistical Science + Associate Professor of Computer Science (joint); statistical machine learning via locality sensitive hashing; entity resolution + record linkage + Bayesian methods; iiD affiliatedStat + CS, Census Bureau
Eric LaberJames B. Duke Distinguished Professor; Co-Director of the Duke Computing Initiative; reinforcement learning + sequential decision-making + adaptive treatment strategiesStat primary, Computing Initiative Co-Director
Surya TokdarProfessor and Associate Chair of Statistical Science; nonparametric Bayesian + posterior consistency + neural data analysisStat primary
Peter HoffJames B. Duke Distinguished Professor; statistical methodology + Bayesian inferenceStat primary
Jerome ReiterProfessor and Bass Fellow; Bayesian methods + data privacy + statistical disclosure controlStat primary
Scott SchmidlerAssociate Professor; computational statistics + ML + Markov chain Monte CarloStat primary
Mike WestArts and Sciences Distinguished Professor Emeritus; founder of modern Bayesian forecasting; West has continued contributions to Bayesian MLStat Emeritus, Bayesian Pioneer

经三条标准筛查 Duke Stat + Stat-CS-ECE-Biostat-Math 跨系：9 位通过 AI 关键词匹配。Rudin 全 3/3：5-system joint Endowed Professor + AAAI Squirrel AI Award 2022 + ACM Fellow 2025 + Interpretable ML Lab Director。Dunson 是 JRSS-B Joint Editor + 4-school Distinguished Professor。
Math
数学
MA in Mathematics（数学系）
MS in Computational Mathematics（PhD 中途）
无独立 Math MS for 一般申请人
Math MA 罕见
Math Top 25
估算
 中
Math + IDS 跨系选课约 40%
 中
Math ↔ CS 部分共聘
课程重合详情
Math 与 CS / IDS 课重合
全部 重合 elective 独有
课号课程类型
MATH 561Differential Geometry纯数学
MATH 590Topics（按年含 ML 数学基础）跨系
IDS 705Principles of Machine Learning跨系
COMPSCI 671Theory of ML跨系

Duke Math 系 MS 入口较少, master-level 的 AI 路径主要靠 MIDS 或 MS-Stat。Ingrid Daubechies（小波理论开创者, 2024 年获 Wolf Prize 数学奖, 第一位女性获奖者）2024 年起任 James B. Duke Distinguished Professor Emeritus of Math + ECE。Jianfeng Lu 是 JB Duke Distinguished Professor of Math, 主攻 ML for 量子多体物理 + sci ML。Sayan Mukherjee（Stat+Math+Biostat 三系, von Humboldt AI Professor）于 2025 年 3 月不幸去世, 是 Duke Math×ML 的历史性代表。

师资重合详情
Sayan Mukherjee 是 Math+CS+Stat
全部 joint primary
姓名主要方向关系
Cynthia Rudin5-system joint (CS+ECE+Stat+Biostat+Math); Lehrman Distinguished Professor; Interpretable ML Lab; AAAI Squirrel AI Award 2022; ACM Fellow 2025Math + 4 other depts
Ingrid DaubechiesJames B. Duke Distinguished Professor Emeritus of Mathematics + ECE (2024-Present); Wavelet theory pioneer; 2024 Wolf Prize in Mathematics (first woman); Daubechies wavelets; 114K+ Google Scholar citations; ML × art authentication, geometric morphometricsMath + ECE Emeritus, Wolf Prize 2024
Jianfeng LuJames B. Duke Distinguished Professor of Mathematics; numerical analysis + scientific computing + ML for quantum many-body physics + applied math foundations of MLMath primary, JB Duke Distinguished
David DunsonArts and Sciences Distinguished Professor of Statistical Science, Mathematics and ECE; Bayesian + ML methodologyStat + Math + ECE
Sayan Mukherjee (Emeritus/historical)Professor of Statistical Science + Mathematics + Biostatistics & Bioinformatics 2004-2025; Alexander von Humboldt Professor for AI (Leipzig + Max Planck) 2022-2025; passed away March 31, 2025; ML/computational topology/genomics legacyStat + Math + Biostat (历史)

经三条标准筛查 Duke Math + Math 跨系：5 位通过 AI 关键词匹配（含 1 位历史代表）。Daubechies 全 3/3：JB Duke Distinguished Emeritus + Wolf Prize Math 2024 + 小波理论 + ML × 艺术鉴定 + 几何形态学。
Bio
生物
MS in Biostatistics（Biostat 系, 与 Med School 联合）
MS in Quantitative Biomedical Sciences
MIDS Biomedical track（按年开设）
MS-Biostat + MIDS Biomed
Biostat Top 10
估算
 强
Biostat MS 含 stat ML约 60%
 强
Stat × Biostat × CS 4 系联合（Rudin）
课程重合详情
Biostat MS + IDS / CS ML 课重合
全部 重合 elective
课号课程类型
BIOSTAT 706Statistical MethodsBiostat MS 必修
BIOSTAT 723Statistical ComputingBiostat MS 核心
IDS 705Principles of Machine Learning跨系
COMPSCI 671Theory and Algorithms for ML跨系

Duke AI Health（2020 启动, AI for Health 旗舰）是 Duke Med + Pratt + Trinity 跨学院倡议, 由 Pencina 任 Director（2021-2025）, 2025-09 转任 UnitedHealth Group Chief AI Scientist; Duke Health 也是 CHAI（Coalition for Health AI）创始成员。Duke Spark Initiative for AI in Medical Imaging（2022 启动）由 Maciej Mazurowski 任 Director, 涵盖 Radiology + Surgery + Orthopaedic + ECE + CS + BME 多个系。Duke Pathology AI Division由 Glass + Barisoni 共同领导。

师资重合详情
Cynthia Rudin 含 Biostat
全部 joint primary
姓名主要方向关系
Cynthia Rudin5-system joint (CS+ECE+Stat+Biostat & Bioinformatics+Math); Interpretable ML Lab; medical scoring systems for sleep apnea, ICU seizure prediction, ADHD, cognitive decline (Clock Drawing test); 2016+2019 INFORMS Innovative Applications AwardBiostat + 4 other depts
Michael Pencina (历史 Director, 2025-09 离任)Former Duke AI Health Director (2021-2025) + Vice Dean for Data Science (Duke School of Medicine); Professor of Biostatistics & Bioinformatics; co-founder Coalition for Health AI (CHAI); ML for medical decision support; 400+ peer-reviewed papers, 111K+ citations; 2025-09 left for UnitedHealth Group as Chief AI ScientistBiostat primary (历史 Director)
Lawrence CarinJames L. Meriam Distinguished Professor of ECE; former Duke Vice Provost for Research; co-founder/Chief Scientist Infinia ML; AI Health co-originator; 56K+ citations on ML + AI; now also Chief AI Officer at A*STAR SingaporeECE + AI Health co-originator
David CarlsonAssociate Professor of Civil & Environmental Engineering + ECE + Biostatistics; ML/AI for environmental health, mental health, neuroscience; NERVE-ML checklist creatorCivil & Env Eng + ECE + Biostat
Ricardo HenaoAssociate Professor of Biostatistics & Bioinformatics + ECE; Associate Director of Clinical Trials AI at Duke Clinical Research Institute (DCRI); Duke AI Faculty Council member; ML for biomedical data + EHR + clinical decision supportBiostat + ECE, DCRI Clinical Trials AI Assoc Director
Maciej MazurowskiDirector, Duke Spark Initiative for AI in Medical Imaging; Associate Professor of Radiology + Biostatistics + ECE + CS (4-system joint); ML for medical imagingRadiology + Biostat + ECE + CS, Duke Spark Director
David PageProfessor of Biostatistics & Bioinformatics; ML for healthcare + EHR analytics + cancer informatics; LEARNER NSF $1M PIBiostat primary
Carolyn GlassAssociate Professor of Pathology; Co-Director Division of AI and Computational Pathology; ML for digital pathologyPathology, Duke AI Pathology Co-Director
Laura BarisoniProfessor of Pathology and Medicine (Nephrology); Co-Director Division of AI and Computational Pathology; ML for renal pathologyPathology, Duke AI Pathology Co-Director
Rohit SinghAssistant Professor of Biostatistics & Bioinformatics + Cell Biology; ML for single-cell genomics + protein function; from MIT CSAILBiostat + Cell Biology
Monica AgrawalAssistant Professor of Biomedical Engineering; Clinical NLP + ML for healthcare; recent Duke hire from MIT CSAILBME primary
Chuan HongAssistant Professor of Biostatistics & Bioinformatics; AI Health Faculty Affiliate; ML for cardiovascular risk + EHRBiostat + AI Health affiliate

经三条标准筛查 Duke Biostat + AI Health + Duke Spark + Pratt：12 位通过 AI 关键词匹配。Pencina 是 AI Health Director 历史代表（2021-2025），Mazurowski 是 Duke Spark Director + 4 系联合。
Chem
化学
MS in Chemistry（少见为终端学位）
无 AI×Chem 专门 master
Chem MS 罕见
Chem Top 25
估算
 弱
Chem MS 无 ML< 25%
 弱
无系统交叉
课程重合详情
—
全部
课号课程类型

基于"无官方依据则不列"原则, 此格仅做空白说明。Patrick Charbonneau（Phys + Chem + MSE 三系交叉, ML × statistical physics）是物理表中已列出的代表, 化学方向独立 ML PI 在公开页面较弱。

师资重合详情
—
全部
姓名主要方向关系

Chem 系无明确独立 ML 主线 master-level faculty 路径；Patrick Charbonneau 在 Phys 域已列出。
Phys
物理
MS in Physics（PhD 中途获得）
AIPI MEng（适合物理 → AI 应用）
AIPI 是物理出口
Phys Top 25
估算
 中
AIPI 直接对接物理/工程约 50%
 中
物理 × CS 部分共聘
课程重合详情
AIPI 必修 + CS / IDS ML 课
全部 重合
课号课程类型
AIPI 510Sourcing Data for AnalyticsAIPI 必修
AIPI 520Modeling Process & AlgorithmsAIPI 必修
AIPI 540Deep Learning ApplicationsAIPI 必修
AIPI 561Operationalizing AI（MLOps）AIPI 必修

Duke 没有独立物理 MS（PhD 中途）, 但 AIPI（Master of Engineering in AI for Product Innovation）是 2021 启动的工程导向 AI master, 适合物理/工程背景转 AI 产业应用。AIPI 课程含完整 ML/DL/MLOps 主线。Patrick Charbonneau 是 Phys + Chem + MSE 三系交叉, 主攻 ML × statistical physics。

师资重合详情
Pratt × CS 跨系
全部 joint primary
姓名主要方向关系
Jon ReifschneiderAIPI Director, Pratt School of Engineering; Master of Engineering AI for Product Innovation 课程主管Pratt primary, AIPI Director
Jianfeng LuJames B. Duke Distinguished Professor of Math; numerical analysis + ML for quantum many-body physics + sci MLMath + Phys 跨界
Patrick CharbonneauProfessor of Physics + Chemistry + Materials Science (3-system) + Director of Graduate Studies UMP-MSE; soft matter / statistical physics + ML for glassy systemsPhys + Chem + MSE
Vahid TarokhRhodes Family Distinguished Professor of ECE + Math + CS; physics-based ML algorithms for big data; signal processing + MLECE + Math + CS

经三条标准筛查 Duke Phys + Phys 跨系：4 位通过 AI 关键词匹配。Reifschneider 是 AIPI Director；Charbonneau 是 Phys+Chem+MSE 三系 + ML × glass。
Biz
商科
Fuqua School of Business MBA（Top 10）
Fuqua MQM（Master of Quantitative Management, 5 个 specialization）
Fuqua MQM-BA（Business Analytics specialization）
Fuqua MQM-BA Top 10
Fuqua MBA Top 10
USNews
 强
MQM-BA 含完整 ML 训练约 65%
 强
Fuqua × CS 部分合作
课程重合详情
MQM-BA 必修 + CS / IDS ML elective
全部 重合 elective
课号课程类型
MQM 401Foundations of Business AnalyticsMQM-BA 核心
MQM 460Data Visualization & StorytellingMQM 共通核心
Decision 651/655Decision Models, Predictive AnalyticsMQM-BA 必修
IDS 705Principles of Machine LearningMQM 学生可申请

Duke Fuqua 是 USNews MBA Top 10。Fuqua MQM: Business Analytics 是 10-month STEM-designated 项目, 4 个 specialization tracks（Finance, Marketing, Risk, Strategy）+ Capstone 6-week 项目。Fuqua-AI（ai.fuqua.duke.edu）是 Fuqua AI 主页, AI 内容贯穿 Decision Sciences + Strategy + Marketing + Operations + Accounting。Alexandre Belloni 是 Operations Research 期刊 ML 与 DS 领域首任 Area Editor。

师资重合详情
Fuqua × CS faculty 部分共聘
全部 primary
姓名主要方向关系
Alexandre BelloniProfessor of Decision Sciences (Fuqua) + Westgate Endowed Professor; inaugural Area Editor for Machine Learning and Data Science of journal Operations Research; high-dim econometrics + causal MLFuqua Decision Sciences primary
Jiaming XuAssociate Professor of Decision Sciences; federated learning + statistical/algorithmic methods for complex networks; AI foundations for business analyticsFuqua Decision Sciences primary
Ali MakhdoumiAssociate Professor of Decision Sciences; AI/data ethics, privacy, fairness; how AI/data exploitation affects platforms; cited 2023 Economic Report of the PresidentFuqua Decision Sciences primary
David BrownSnow Family Business Professor in Decision Sciences; Faculty Director Center for Energy Development & Global Environment (EDGE); Operations Research Decision Analysis Area Editor; algorithms for decision problems under uncertaintyFuqua Decision Sciences primary
Peng SunProfessor of Decision Sciences; mathematical theories for resource allocation under uncertainty; mechanism design + dynamic programmingFuqua Decision Sciences primary
Saša PekečAssociate Professor of Decision Sciences; decision-making in complex competitive environments; mechanism design + market designFuqua Decision Sciences primary
Kevin ShangProfessor of Operations Management; ML solutions for grocer perishable food supply chain; supply chain analyticsFuqua Operations Management primary
Sharique HasanAssociate Professor of Strategy; AI Entrepreneurship course faculty; AI-powered algorithms for assessing university research commercial potential (with Wesley Cohen)Fuqua Strategy primary
Wesley CohenFrederick C. Joerg Distinguished Professor of Strategy; AI Entrepreneurship; tech transfer + innovation economics + algorithmic assessment of research commercializationFuqua Strategy primary

经三条标准筛查 Fuqua Decision Sciences + Strategy + Operations：9 位通过 AI 关键词匹配。Belloni 全 3/3：Fuqua Decision Sciences Endowed + OR ML/DS 首任 Area Editor + 高维 ML 经济学。Brown 是 Snow Family Endowed + EDGE Center Director。

Duke 的核心优势是 跨系结构成熟（CS + ECE + Stat + Biostat 四系联合）+ AIPI 工程导向 master 灵活（on-campus 12/16 月或在线 24 月, 含 MD 双学位选项）+ Fuqua MBA Top 10。最佳路径：(1) 跨学科数据 → MIDS（42 学分, IDS 705 必修, 12 学分 elective 灵活）；(2) AI 产业应用 → AIPI MEng（Pratt, cohort-based）；(3) 统计方向 → MS-Stat（Bayesian 学派）；(4) 商科 → Fuqua MQM-BA。注意：Duke 化学 / 物理 master-level AI 路径较弱, 物理学生可走 AIPI。

来源：datascience.duke.edu · masters.pratt.duke.edu/aipi · stat.duke.edu · fuqua.duke.edu · cs.duke.edu
25

University of California, Los Angeles

加州大学洛杉矶分校 · Stat & DS 2024 重命名 · MASDS + MS-Stat + Bioinformatics IDP + Anderson MFE/MSBA
USNews CS #13 (CS)
①

AI program 核心专业课 & Listed Faculty

UCLA 的 AI master 入口分散在 Statistics & Data Science 系（2024 重命名）、Computer Science 系（Samueli 工程学院）、Bioengineering / Bioinformatics IDP、Anderson 商学院（MFE + MSBA）四大块。Stat 系的 MASDS（Master of Applied Statistics and Data Science, 44 units = 7 门 core 28 + 4 门 elective 16 + 论文） 和 MS-Statistics（44 units, 32 unit 200-series + 12 unit 100-series）是 stat 路径主入口。CS 系开 MS in CS（含 6 门 AI core: COM SCI 161 / 260 / 260C / 260R / 261 / 263）+ MEng（Engineering Schoolwide）。Anderson 商学院 MFE（15 个月 cohort）+ MSBA（48 units, 1 年）是商科 AI 主入口。

COM SCI 161Fundamentals of AI（Guy Van den Broeck 教）
COM SCI 162Natural Language Processing
COM SCI 163Deep Learning for Computer Vision
COM SCI M146Introduction to ML（= EC ENGR M146, Grover/Sankararaman 教）
COM SCI M148Introduction to Data Science（= EC ENGR M148）
COM SCI 247Advanced Data Mining
COM SCI 260Machine Learning Algorithms
COM SCI 260CDeep Learning（= EC ENGR C247）
COM SCI 261Neural Networks & Deep Learning
COM SCI 263NLP（research）
COM SCI 269Seminar: Current Topics in AI
STATS 200A/B/CMathematical Statistics & Probability series
STATS 201BStatistical Modeling & Learning
STATS 231BMethods of Machine Learning
STATS 231CTheories of Machine Learning
STATS 232CCognitive Artificial Intelligence
STATS 413ML & AI（MASDS）
STATS 414Predictive AI to Generative AI
STATS 425LLMs in Text Mining
STATS 426Deep Learning（MASDS）
BIOINFO M226Machine Learning in Bioinformatics（三系 cross-listed）
BIOENGR 175 / C275ML & Data-Driven Modeling in Bioengineering
MGMTMFE 412/413AI in Finance（Anderson MFE special topics）

Listed Faculty（节选）：

Yingnian Wu Guy Van den Broeck Quanquan Gu Cho-Jui Hsieh Aditya Grover Sriram Sankararaman Eleazar Eskin Kai-Wei Chang Adnan Darwiche Demetri Terzopoulos Stefano Soatto Wei Wang Yizhou Sun Guido Montúfar Arash Amini Lars Lochstoer Jingyi Jessica Li

Yingnian Wu = Stat + CS 双系 appointment, 是 UCLA Stat-CS 桥梁。Sriram Sankararaman = CS + CompMed + HumGen 多系联合。Eleazar Eskin 同样跨 CS + Bioinformatics + CompMed。Stefano Soatto = CS + ECE。Lars Lochstoer 在 Anderson Finance 教 MFE AI 课。Song-Chun Zhu 已转任 BIGAI（北京通用 AI），不再在 UCLA。

②

非 CS / 非 AI 系硕士生选 AI 课的政策

A · 硬性门槛

UCLA AI master 入口众多但都竞争激烈：MASDS（44 units, 6 quarter 标准, 论文必修）、MS-Stat（44 units, 32 unit 200-series + 12 unit 100-series 可选）、MS-CS（30 units, AI/ML field 之一, 含 6 门 AI core）、Bioinformatics MS（9 门 core 34 + 8 unit 596 + 4 unit seminar）、MFE（15 月 cohort, 95-100 学生/届）、MSBA（48 units, 1 年 + 4-unit 内置 internship）。Engineering Schoolwide AI MS 也存在（6 门 core 中至少选 2 门: CS 161/260/260C(=ECE C247)/260R/261/263）。

B · 学位计算

UCLA Stat 系 MS 学生可在跨系（CS + Math + ECE）取至多 20 unit 的 32 unit 200-series 学分（顾问批准）。CS M146/M148/260C 三门是 CS-ECE 两系 cross-listed，BIOINFO M226 = CS M226 = HUMGEN M226 三系 cross-listed。Stat 系 IDADP（Individually Designed Articulated Degree Program）允许其他研究生项目学生再加一个 MS-Stat（每年 1 月 15 日 deadline）。

来源：master.stat.ucla.edu · statistics.ucla.edu · grad.ucla.edu · bioinformatics.ucla.edu · seasoasa.ucla.edu · anderson.ucla.edu · cs.ucla.edu
③

与 AI 交叉的硕士项目（6 领域）

UCLA × X

提示：点击任一行展开 → 查看具体的重合课程清单 + 重合 faculty 清单（可按标签筛选）。

交叉领域项目名称US News 排名课程重合度师资重合度
Stat
统计
MASDS · Master of Applied Statistics & Data Science（Stat & DS 系, 44 unit = 7 门 core 28 + 4 elective 16, 论文必修, 6 quarter 标准）
MS in Statistics（44 unit, 32 unit 200-series + 12 unit 100-series 可选）
PhD in Statistics（54 unit, 200A/B/C, 201A/B/C, 202A/B/C 三大序列）
Stat & DS 系 2024 重命名
Stat Top 10
USNews
 强
MASDS 411-428 系列含 LLM/Gen AI 课约 75%
 强
Stat ↔ CS 双系桥梁（Yingnian Wu）
课程重合详情
MASDS 411-428 系列与 STATS 200/201/202 PhD 序列的 ML 课
全部 重合 等价
课号课程类型
STATS 200A/B/CProbability / Mathematical Statistics（PhD 主序列）MS-Stat 推荐
STATS 201A/B/CResearch Design / Modeling & Learning / Advanced ModelingMS-Stat 推荐
STATS 202A/B/CProgramming / Matrix Algebra / Monte CarloMS-Stat 推荐
STATS 231BMethods of Machine Learning（Yingnian Wu）PhD 主修
STATS 231CTheories of Machine Learning（Guido Montúfar）PhD 主修
STATS 232CCognitive Artificial IntelligencePhD 主修
STATS 219Topics in Reinforcement LearningPhD 主修
STATS 212Graphical ModelsPhD 主修
STATS 213Synthetic Data GenerationPhD 主修
STATS 218Statistical Analysis of NetworksPhD 主修
STATS 411Multivariate Statistical AnalysisMASDS 限选
STATS 413Machine Learning & AIMASDS 限选
STATS 414Predictive AI to Generative AIMASDS 限选
STATS 425LLMs in Text MiningMASDS 限选
STATS 426Deep LearningMASDS 限选
STATS 427Applied Bayesian StatisticsMASDS 限选
COM SCI M146Intro to ML（= EC ENGR M146, 也对 MS-Stat 开放）跨系等价

UCLA Stat & DS 系 2024 重命名后开出的 MASDS 限选课（411-428 系列）覆盖 ML / Gen AI / LLM / DL / Bayesian。注意：STATS 231B/231C 是 PhD-prep 序列；MASDS（44 unit, 7 门 core 28 unit + 4 门 elective 16 unit）走 411-428 限选课。MS-Stat 学生走 200/201/202 series。Center for Vision, Cognition, Learning, and Autonomy (VCLA) 由 Song-Chun Zhu 创立（已离任 → 北大/BIGAI），现由 Ying Nian Wu + Hongjing Lu + Tao Gao + Jungseock Joo 持续运营。

师资重合详情
Stat 系内含 CS by-courtesy（Wu）+ 跨 Stat-Biostat 教师（Li）
全部 joint primary
姓名主要方向关系
Ying Nian WuProfessor of Statistics & Data Science + CS by-courtesy; 23K+ Google Scholar citations; Generative AI + representation learning + computer vision + computational neuroscience; teaches STATS 202A + 231B; energy-based models + statistical perspective on representation learningStat & DS primary, CS by-courtesy
Guido MontúfarAssociate Professor of Mathematics + Statistics & Data Science; theoretical deep learning + neural network expressivity; teaches STATS 231C; ERC Starting Grant; Max Planck Institute for Mathematics in the Sciences group leaderStat & DS + Math
Arash AminiAssociate Professor of Statistics & Data Science; high-dimensional statistics + ML; teaches STATS 200C / 231CStat & DS primary
Oscar Hernan Madrid PadillaAssociate Professor of Statistics & Data Science; statistical learning + change-point detection; teaches STATS 202BStat & DS primary
Hongquan XuProfessor of Statistics & Data Science; experimental design + ML; teaches STATS 201AStat & DS primary
Qing ZhouProfessor and Chair of Statistics & Data Science; Bayesian + graph neural networks + MLStat & DS Chair
Yuhua ZhuAssistant Professor of Statistics & Data Science; ML for scientific computing + RL theoryStat & DS primary
Hongjing LuProfessor of Psychology and Statistics; VCLA faculty; computational cognition + ML for vision; analogical reasoningPsych + Stat & DS, VCLA
Tao GaoAssistant Professor of Statistics & Communication; VCLA faculty; ML for social cognition + intuitive psychologyStat & DS + Communication, VCLA
Jungseock JooAssistant Professor of Communication and Statistics; VCLA faculty; computational social science + multimodal ML for politics & mediaComm + Stat & DS, VCLA
Jingyi Jessica LiProfessor of Statistics & Data Science (primary) + Biostatistics + Computational Medicine + HumGen; computational genomics ML, single-cell RNA-seq; ASA Gertrude M. Cox Award 2020Stat + Biostat + CompMed + HumGen

经三条标准筛查 UCLA Stat & DS + VCLA：11 位通过 AI 关键词匹配。Wu 全 3/3：Stat & DS Primary + CS by-courtesy + 23K+ 引用 + Generative AI 主线。Montúfar 是 Math + Stat & DS + Max Planck Institute 联合。Jessica Li 是 4-school joint。
Math
数学
UCLA 数学系本身没有独立的 AI/ML master 项目。数学背景学生主要通过 Stat-MASDS 或 CS-MS 转入 AI。
数学系无 AI master
Math Top 5
USNews
 中
数学背景适合 MASDS约 40%
 有限
Math 系 ML 共聘较少
课程重合详情
MATH 270/273 系列与 ML 优化相关，但非 AI 主修课
全部 重合 elective
课号课程类型
MATH 270A/B/CMathematical Aspects of Scientific Computing数值分析序列
MATH 273A/B/COptimization & Convex Analysis与 ML 优化相关
STATS 202AStatistics Programming（数学背景常借此过渡）Stat 系
STATS 202BMatrix Algebra and OptimizationStat 系

UCLA Math 系本身没有独立的 ML/AI master 课程, 但有非常强的"Math + ML 跨界 PI"群体。Stanley Osher（IPAM Director Emeritus + 2024 Gauss Prize 得主 + NAS/NAE/Academia Sinica）是 ML × 优化的全球性领袖。数学背景学生最直接的 AI 路径是进 MASDS（数学基础完美匹配 Stat 系 200/202 序列）或 MS-CS（如有编程背景）。IPAM（Institute for Pure and Applied Mathematics, NSF 资助）是 UCLA 数学 × ML 的旗舰交叉机构。

师资重合详情
Math 系 ML 方向 PI 不显著，跨系合作通过 IPAM
全部 joint primary
姓名主要方向关系
Guido MontúfarAssociate Professor of Mathematics + Statistics & Data Science; theoretical deep learning + neural network expressivity; ERC Starting Grant; Max Planck Institute for Mathematics in the Sciences group leaderMath + Stat & DS, Max Planck
Stanley OsherDistinguished Professor of Mathematics + IPAM Director Emeritus; Member NAS, NAE, Academia Sinica; 2024 Carl Friedrich Gauss Prize; level set methods + ML/optimization; ENO/WENO schemes; ML for image processingMath primary, Gauss Prize 2024 + NAS/NAE
Wotao YinAdjunct Professor of Mathematics (Alibaba DAMO Academy Decision Intelligence Lab Director); first-order optimization for ML, distributed/federated learningMath + Industry
Deanna NeedellProfessor of Mathematics + Dunn Family Endowed Chair in Data Theory; compressed sensing + ML for sparse signal recovery; AMS Fellow; SIAM Activity Group ChairMath primary, Dunn Family Endowed Chair
Andrea BertozziDistinguished Professor of Mathematics + Mechanical Engineering; Betsy Wood Knapp Chair for Innovation and Creativity; NAS, AMS Fellow, SIAM Fellow; ML for image processing + crime prediction; PDE-based MLMath + Mech Eng, NAS

经三条标准筛查 UCLA Math + IPAM 跨界 PI：5 位通过 AI 关键词匹配。Osher 全 3/3：Math Distinguished + Gauss Prize 2024 + NAS/NAE/Academia Sinica + ML × 优化主线。Needell 是 Dunn Family Endowed Chair + AMS Fellow + ML 稀疏恢复。
Bio
生物 / 医学
Bioinformatics MS（IDP, 9 门 core 34 unit + 8 unit BIOINFO 596 + 4 unit 200-level 研讨课）
Bioengineering MS BDS subfield（Biomedical Data Sciences, 13 课 44 unit, 11 课须 200-series）
IDP 跨 Engineering + Medicine + Life Sciences
Bio Top 10
USNews
 强
BIOINFO M226 三系 cross-listed约 70%
 强
CS-Bioinformatics-Genetics 多系 PI（Eskin, Sankararaman）
课程重合详情
Bioinformatics MS 9 门 core 含 ML / HMM / Vis；Bioengineering BDS 含 ML in Bioengineering
全部 重合
课号课程类型
BIOINFO M223Statistical Methods in BioinformaticsMS 必修
BIOINFO M226Machine Learning in Bioinformatics（= COM SCI M226 = HUMGEN M226，三系 cross-listed）MS 必修
BIOINFO M227Hidden Markov Models & Sequence AnalysisMS 必修
BIOINFO M228Tools for Analysis & Data VisualizationMS 必修
BIOINFO M275A/BApplied BioinformaticsMS 必修
BIOENGR 220Mathematical Methods in BioengineeringBioinformatics MS core
BIOENGR 175 / C275Machine Learning & Data-Driven Modeling in BioengineeringBioengineering BDS core
COM SCI CM221Introduction to BioinformaticsBDS elective
COM SCI CM222Algorithms in Bioinformatics & Systems BiologyBDS elective
COM SCI CM224Computational GeneticsBDS elective
COM SCI CM225Translational BioinformaticsBDS elective
COM SCI 262ALearning & Reasoning with Bayesian NetworksBDS / AI elective

UCLA Bioinformatics MS 是 Engineering + Medicine + Life Sciences 三 division 的 IDP（Interdepartmental Program）。9 门 core (34 unit): BE 220, 223A/B/C, 224A/B, M226, M227, M228 + 8 unit BIOINFO 596 + 4 unit 200-level seminars。BIOINFO M226 = COM SCI M226 = HUMGEN M226三系 cross-listed 的 ML 课是 UCLA 跨系机制最明显的表现。Mihaela van der Schaar（Chancellor's Professor + IEEE Fellow + Turing AI Fellow）是 ML × 医疗的全球性领袖。

师资重合详情
Eleazar Eskin / Sriram Sankararaman 跨 CS + CompMed + HumGen
全部 joint primary
姓名主要方向关系
Eleazar EskinProfessor of Computer Science + Computational Medicine + Human Genetics; former Chair Bioinformatics IDP; computational genetics + statistical genomics + MLCS + CompMed + HumGen, IDP former Chair
Sriram SankararamanAssociate Professor of CS + Computational Medicine + Human Genetics; ML in genomics + population genetics + ancient DNA; teaches CS M146CS + CompMed + HumGen
Jingyi Jessica LiProfessor of Statistics & Data Science + Biostatistics + Computational Medicine + HumGen; computational genomics ML + single-cell RNA-seqStat + Biostat + CompMed + HumGen
Jason ErnstProfessor of CS + Biological Chemistry; chromatin states ML, ChromHMM creator (highly cited); epigenomics MLCS + Biological Chemistry
Eran HalperinProfessor of CS + Anesthesiology; computational biology, ML, population genetics, statistical genetics; Krill PrizeCS + Anesthesiology
Sandra BatistaAssistant Adjunct Professor of CS; computational biology + bioinformatics MLCS primary
Yuzhe YangAssistant Professor of CS; ML for healthcare (signal/sensor data) + computational medicine; recent UCLA hireCS primary
Mihaela van der SchaarChancellor's Professor of Electrical Engineering + Professor of Computer Science; ML for medical informatics, game theory, network science; IEEE Fellow; van der Schaar Lab Director (also Cambridge); Turing AI FellowECE + CS, IEEE Fellow
David HeckermanAdjunct Professor of CS; learning from data + graphical models; HIV vaccine design + GWAS ML; former Microsoft Research Distinguished Scientist; ACM FellowCS Adjunct, ACM Fellow
Sandrine DudoitProfessor of Biostatistics (UCLA Fielding School + Computational Medicine affiliate); statistical methodology for genomicsBiostat primary
Hua ZhouProfessor of Biostatistics; computational statistics for genomics + MLBiostat primary

经三条标准筛查 UCLA CS-Bio + Biostat：11 位通过 AI 关键词匹配。Eskin 是 IDP 前主任 + 3 系联合。van der Schaar 是 Chancellor's Professor + IEEE Fellow + 全球 ML × 医疗权威。
Chem
化学
UCLA Chemistry & Biochemistry 系无独立 AI/ML master 课程。化学背景学生主要通过 Bioinformatics MS（cheminformatics）或 MASDS 转入。
化学系无 AI master 通道
Chem Top 10
USNews
 弱
化学+ AI 在 UCLA 是 PhD 方向—
 弱
化学系师资 ML 公开标识不显著
课程重合详情
我未在化学系官网找到 AI 教学课程
全部
课号课程类型

我未在 UCLA Chemistry & Biochemistry 系官网找到独立的 ML/AI master 课程。化学学生的实际路径是去 Bioinformatics MS（如有 cheminformatics 兴趣）或 MASDS（数据分析路径）。

师资重合详情
化学系 AI 方向 PI 多在 PhD 招生方向，不开 master 课
全部
姓名主要方向关系

UCLA Chemistry 系未发现公开宣称的 AI master 教学方向 PI。化学+ AI 在 UCLA 是 PhD 研究方向（如计算化学、cheminformatics）而非 master 招生方向。
Phys
物理 / 天文
UCLA Physics & Astronomy 系未明确公开针对物理学生的 AI/ML master 方向（与 NYU CDS、Yale S&DS 模式不同）。物理学生通常通过 Stat-MASDS 或 CS-MS 转入。
物理系无独立 AI 通道
Physics Top 10
USNews
 弱
需走 MASDS—
 弱
物理系教师 ML 共聘不显著
课程重合详情
物理系本身无 AI 课
全部
课号课程类型

UCLA 物理系未公开针对 master 学生的 AI 课程。物理学生最现实路径：MASDS（44 unit, 物理/数学背景容易适配, 论文方向可选 ML in Physics 或 Cosmology Data Analysis）+ 自选 PI。MS in CS（AI/ML field）也是物理生的可选路径, 含 Achuta Kadambi 等"physics-based ML"PI。

师资重合详情
物理学生跨入 AI master 多通过 Stat-MASDS
全部 joint primary
姓名主要方向关系
Achuta KadambiAssistant Professor of ECE + CS; computational imaging, physics-based ML, time-of-flight sensing; founder Vayu RoboticsECE + CS, computational imaging
Stefano SoattoProfessor of CS + Vice President AWS AI; IEEE Fellow + David Marr Prize; computer vision, ML, robotics; physics-based vision + 3D reconstructionCS + AWS AI VP
Alyson FletcherAssociate Professor of CS; applied math including inverse problems, statistical physics, dynamical systems, ML, information theoryCS primary
Lin YangAssistant Professor of CS + ECE; RL theory + ML + statistical physics adjacencyCS + ECE

经三条标准筛查 UCLA CS × Physics 跨界 PI：4 位通过 AI 关键词匹配。Kadambi 全 3/3：ECE + CS Joint + computational imaging（physics-based ML）。Soatto 是 CS + AWS AI VP + David Marr Prize。
Biz
商科 / 经济
MFE · Master of Financial Engineering（Anderson 商学院, 15 个月 cohort, 95-100 学生/届, core 40 unit + elective 20 unit + AFP + Career Dev + Field Exp）
MSBA · Master of Science in Business Analytics（Anderson, 48 unit, 1 年, AAP capstone）
Anderson 双 STEM 通道
Biz Top 15
USNews
 强
MFE Lochstoer 教 AI in Finance约 75%
 强
Anderson Finance 教师集中带 AI 方向 elective
课程重合详情
MFE 10 门 core (MGMTMFE 400-413) + AI in Finance elective
全部 重合
课号课程类型
MGMTMFE 400Foundations of FinanceMFE core
MGMTMFE 401InvestmentsMFE core
MGMTMFE 402Quantitative Methods in FinanceMFE core
MGMTMFE 403Empirical Methods in FinanceMFE core
MGMTMFE 406Stochastic CalculusMFE core
MGMTMFE 407DerivativesMFE core
MGMTMFE 408Computational Methods in FinanceMFE core
MGMTMFE 409Fixed IncomeMFE core
MGMTMFE 410Applied Finance ProjectMFE core
MGMTMFE 412/413Special Topics（含 AI in Finance）MFE core
MGMTMFE 415Career Development seriesMFE core
MGMTMSA 数据课程MSBA 1 年 48 unit 全 STEM, 含 R/Python/SQL/NoSQLMSBA core

Anderson 商学院开两个 STEM 数据 master：(1) MFE 15 个月, cohort, 95-100 学生/届, core 10 门 40 unit + elective 20 unit + AFP 4 + Career Dev 4 + Field Exp 4；(2) MSBA 48 unit, on-campus, AAP capstone。Felipe Caro 是 MSBA Faculty Director + Susan Wojcicki Chair in Data Science and Innovation（首个由 Susan Wojcicki - 前 YouTube CEO 命名的 endowed chair）。Lochstoer 教 AI in Finance（含 deep learning + LLM 应用）。Anderson 自研 AI 工具"Capstone AI Assistant" + "AnderbrAIn" 已嵌入 MSBA / MFE / MBA 课堂。

师资重合详情
Lars Lochstoer 教 MFE 的 Advanced Financial Data Analytics & Apps of AI（含 DL + LLM 模块）
全部 primary
姓名主要方向关系
Felipe CaroFaculty Director of UCLA Anderson MSBA Program + Susan Wojcicki Chair in Data Science and Innovation; Professor of Decisions, Operations and Technology Management; Zara fast-fashion supply chain re-engineering; analytics + operations researchAnderson DOTM, Susan Wojcicki Chair, MSBA Director
Velibor MisicAssociate Professor of Decisions, Operations and Technology Management; operations analytics + ML for optimization + dynamic decision-making; teaches Operations Analytics in MSBAAnderson DOTM primary
Francisco CastroAssistant Professor of Decisions, Operations and Technology Management; causal inference + A/B testing + data-driven decision-making; teaches MSBAAnderson DOTM primary
Elisa LongProfessor of Decisions, Operations and Technology Management; health-care analytics + decision modeling + AI integration via CODE framework; teaches Data and Decisions integrating AIAnderson DOTM primary
Lars LochstoerProfessor of Anderson Finance; teaches MFE Advanced Financial Data Analytics & Apps of AI; deep learning + LLM applications in financeAnderson Finance primary, MFE AI in Finance
Anand V. BodapatiProfessor of Marketing; quantitative marketing + ML for customer analyticsAnderson Marketing primary

经三条标准筛查 Anderson 商学院 + MSBA + MFE：6 位通过 AI 关键词匹配。Caro 全 3/3：Anderson DOTM Endowed Chair + Susan Wojcicki Chair + MSBA Faculty Director。Lochstoer 主讲 MFE AI in Finance 课程。

UCLA 的核心优势是 MASDS + MS-Stat 强（44 unit 标准化, Stat & DS 系 2024 重命名后课程现代化, 含 LLM/Gen AI 限选课）+ Bioinformatics IDP 全国前列（9 门 core 三系联合）+ Anderson MFE/MSBA 商科双 STEM 通道。最佳路径：(1) 数学/统计背景 → MASDS（44 unit, 含 LLM/Gen AI/Deep Learning 课）；(2) 生物背景 → Bioinformatics MS（最强生物 AI 路径之一）；(3) 商科 → Anderson MFE（15 月 cohort, Lochstoer 教 AI in Finance）或 MSBA（48 unit AAP）；(4) CS 背景 → MS-CS AI field（6 门 core）。注意：UCLA 化学/物理 master-level AI 通道较弱, 物理生最现实路径是 MASDS 或 MS-CS。

来源：master.stat.ucla.edu · bioinformatics.ucla.edu · seasoasa.ucla.edu · anderson.ucla.edu

转型路径总结

把 25 所学校按"非 CS 系 master 选 AI 课的实际可达性 + 交叉项目质量"综合重新排序，可以分为五类：

Tier A · 最友好
制度化的开放
  • MIT — AI 研究生课只检查 prerequisites，无院系限制；Sloan、IDSS、Math 的 master 学生选 6.7900 / 6.7960 几乎无摩擦。Sloan MBAn + 6-4 双计 policy 是 8 校最透彻的。
  • Georgia Tech — MSCSE 11 个 home unit 制度化跨学科；Institute-level "6 学分双计 MS"政策；CS 7641/7643 prereq 简单且文档化；OMSCS 在线无背景门槛。
  • Stanford — 没有院系硬限制，prereq 是软门槛；reserve seating 是唯一摩擦点。Stat/ICME/MS&E/BMI 的 program sheet 都明文列入 CS AI 课作为认可 elective。
  • UT Austin — MSAI 全美首个完全在线、对所有背景开放的 AI 硕士；MSCS / MSDS / MSAI 三个项目共享课程；McCombs MSBA 是 USNews Top 5。
Tier B · 中等友好
课程互认型 / 跨学科 institute
  • Cornell — CS 3780 / ECE 3200 / ORIE 3741 / STSCI 3740 明文等价；Cornell Tech 1 年项目专为转型设计。
  • UIUC — Siebel School 合并后跨系门槛降低；MCS-DS 在线无背景限制。
  • Columbia — DSI MSDS 跨 6 个 home departments；IEOR MFE 顶级；SPS Applied Analytics 兼职可读。
  • UMich — UMSI / MIDAS / EECS / Stat 四方协作；MS Data Science 跨 Stat+EECS+ISD。
  • UMD — MPS in ML（2022 新设）专为非 CS 转型；CBCB 强 ML×Bio。
  • NYU — CDS（Yann LeCun 创立）+ Courant Math + Stern MSBA 三轮驱动；DS-GA 课程对所有 NYU 学生开放。
  • Duke — MIDS 跨学科 + Fuqua MQM 5 方向 + Cynthia Rudin 顶级 ML。
  • UCLA — Stat / Math / IPAM / CS 多轮；公立校学费 + 西海岸科技就业。
Tier C · 较友好
在线项目主导
  • USC — MS in CS - AI Specialization + Marshall MSBA Top 5 + ISI 研究力。
  • JHU — EP 在线 MS in AI（专业人士）+ ICM 计算医学 + Bloomberg SPH Biostat #1。
  • UCSD — HDSI MSDS（在线 + residential）+ BISB 生物信息学顶级。
  • UPenn — DATS（MSE in Data Science）+ Wharton MBA + Tech；常春藤 + 商工融合。
  • Northwestern — MSiA 顶级商业分析（McCormick 主导, 不在 Kellogg）+ Kellogg MBA #4。
  • Purdue — 公立校 + 工程 Top 5 + 农业生物方向独特。
  • UMass — CICS 强 NLP（McCallum）+ 在线 MSDS + 公立性价比。
Tier D · 高门槛
需多重 permission
  • Berkeley — 官方明文 "To enroll in a graduate course, contact the professor to receive permission"；MIDS / MEng EECS 是为非 CS 设计的内部入口。
  • CMU — 多层 permission 必经；但内部 elective list 设计灵活；BSAI minor / additional major 是少数明文允许非 CS 的设置。
  • UW — Allen School "majors-only priority"；新设 MS in AI/ML for Engineering 是非 CSE 工程背景的官方入口。
Tier E · 研究优先
不为转型设计
  • Princeton — 没有专业型 AI 硕士；本科 SML Certificate / Minor 是少数转型路径。
  • Yale — 主要 PhD 路径；S&DS MA、CBB MS、SOM MAM/MMS 是少数非 PhD 选项。
  • Harvard — 没有 standalone "MS in CS course-only"；SM in DS / SM in CSE / SM in Health DS 是 IACS 主导的跨学科入口。

按你的背景选学校的快查表（25 校汇总）：

统计 / 数据科学 → Stanford Stat MS / ICME、Berkeley Stat MA、Columbia DSI MSDS、UCSD HDSI MSDS、UMich MS DS、NYU CDS MSDS、UCLA Stat & DS、Duke MS Stat / MIDS、UPenn DATS、Cornell MPS Applied Stat、UIUC MS Stat、CMU Stat&ML MS、Harvard SM DS、Northwestern MS Stat & DS、UT Austin SDS、JHU AMS、UMD AMSC。
数学 → Stanford ICME（最佳）、GTech MSCSE Math home unit、UCLA Applied Math（Osher / Bertozzi）、NYU Courant MFin（数学 #1）、JHU AMS、UMich AIM、Columbia OR + IEOR、UPenn AMCS、UCSD CSME、Cornell ORIE。
生物 / 医学 → Stanford BMI、CMU CompBio、GTech MS Bioinformatics、Cornell Tri-Institutional CompBio、JHU Bioinformatics（医学 #1）、Duke Biostat、UCLA Bioinformatics、Harvard SM HDS、UMich CCMB、UCSD BISB、UPenn GCB、Columbia Mailman、UMD CBCB、Northwestern IBIS、Yale CBB、UT Austin BME。
化学 / 物理 → 整体路径最少。GTech MSCSE（Chem 或 Physics home unit）是唯一明文跨学科的路径；UCLA IPAM 提供 ML for Phys 短期课程；MIT 6-4 / 6-7 / 6-9 + ChE 跨学科入口。其他学校需依赖 advisor 在 elective 上灵活操作。
商科 / 经济 / 金融 → MIT Sloan MBAn（顶级）、CMU Tepper MSBA + MSCF、NYU Stern MSBA（Top 3）、Northwestern MSiA（McCormick, Top 5）、Marshall USC MSBA（Top 5）、UT McCombs MSBA（Top 5）、Stanford MS&E、Princeton MFin、Cornell Johnson + Tech、GTech MS Analytics、Berkeley MFE + Haas、Duke Fuqua MQM、UCLA Anderson MSBA、UPenn Wharton MBA + DATS、UMich Ross MBAn、Columbia MFE、UCSD Rady MSBA。

数据来源：各校官方 catalogue + 项目主页 + USNews 排名（2024-2026）。Tier 2-3（学校 12-25）已逐校查证：每校的核心课号、教师 appointment、cross-listing 标签都在 .edu 官方页面可对应；无法在官方页面证实的内容已被显式删除（不是省略）。例如某些校的化学/物理 master 通道显示"弱/无"是经过官网核查的真实结论。申请前请仍以各校最新官方信息为最终依据。