At Stanford:
| Course Code | Course Name | Instructor |
|---|
| CME 308 | Stochastic Methods in Engineering | Peter Glynn |
| ECON 272 | Intermediate Econometrics III: Methods for Applied Econometrics | Guido Imbens |
| EE 364A | Convex Optimization I | Stephen Boyd |
| STATS 300A | Theory of Statistics I | Joseph Romano |
| STATS 300B | Theory of Statistics II | Tselil Schramm |
| STATS 300C | Theory of Statistics III | Emmanuel Candes |
| STATS 305A | Applied Statistics I | Trevor Hastie |
| STATS 310A | Theory of Probability I | Persi Diaconis |
| STATS 361 | Causal Inference | Stefan Wager |
| STATS 371 | Applied Bayesian Statistics | Wing H Wong |
| CME 302 | Numerical Linear Algebra | Eric Darve |
| MS&E 319 | Matching Theory | Amin Saberi |
| MS&E 379 | Social Data Analysis | Charles Eesley |
| STATS 209 | Introduction to Causal Inference | Dominik Rothenhaeusler |
At NYU:
(-Ux stands for undergraduate level; -Gx stands for graduate level)
Probability & Statistics
| Course Code | Course Name | Instructor |
|---|
| DS-GA 3001 | Applied Statistics | Yanjun Han |
| DS-GA 1020 | Mathematical Statistics | Jonathan Niles-Weed |
| DS-GA 3001 | Probability and Statistics 2 | Carlos Fernandez-Granda |
| STAT-UB 21 | Introduction to Stochastic Processes | Halina Frydman |
| DS-UA 9201 | Causal Inference | Judith Abécassis |
| MATH-UA 235 | Probability and Statistics | Antoine Cerfon |
| STAT-UB 3 | Regression and Forecasting Model | Lucius Riccio |
Statistical & Machine Learning
| Course Code | Course Name | Instructor |
|---|
| MATH-GA 2840 | Theory of Deep Learning | Arthur Jacot |
| DS-GA 3001 | Modern Topics in Statistical Learning Theory | Qi Lei |
| CSCI-GA 3033 | Machine Learning for Healthcare | Rajesh Ranganath |
| DS-UA 301 | Advanced Techniques in Machine Learning and Deep Learning | Parijat Dube |
| CSCI-UA 475 | Predictive Analytics | Anasse Bari |
| CSCI-UA 473 | Introduction to Machine Learning | Lerrel Pinto |
Data Science
| Course Code | Course Name | Instructor |
|---|
| CSCI-UA 479 | Data Management and Analysis | Matthew Zeidenberg |
| DS-UA 202 | Predictive Analytics | Elisha Cohen |
| DS-UA 112 | Introduction to Machine Learning | Pascal Wallisch |
Mathematical & Algorithmic Foundations
| Course Code | Course Name | Instructor |
|---|
| MATH-UA 325 | Real Analysis | Michal Shavit |
| MATH-UA 140 | Linear Algebra | Simon Becker |
| MATH-UA 123 | Multivariable and Vector Calculus | Daniel Stein |
| MATH-UA 120 | Discrete Mathematics | Hasan Oveys |
| CSCI-UA 310 | Algorithms | Areeba Ikram |
| CSCI-UA 201 | Computer Systems Organization | Hasan Aljabbouli |
| CSCI-UA 102 | Data Structures | Anasse Bari |
