Course and teaching

Courses

• IEOR 262A Mathematical Programming I, by Alper Atamturk

• IEOR 262B Mathematical Programming II, by Javad Lavaei

• IEOR 263A Applied Stochastic Process I, by Rhonda Righter

• IEOR 263B Applied Stochastic Process II, by Xin Guo

• IEOR 266 Network Flows and Graphs, by Dorit Hochbaum

• IEOR 258 Control and Optimization for Power Systems, by Javad Lavaei

• IEOR 250 Supply Chain and Logistics Management, by Max Shen

• IEOR 290 Special topics: Optimization for machine learning, by Paul Grigas

• Stats 210A Theoretical Statistics I, by Will Fithian

• Stats 210B Theoretical Statistics II, by Martin Wainwright

• Stats 215A Statistical Models: Theory and Application; by Bin Yu

• Stats 241A/CS 281A Statistical Learning Theory; by Benjamin Recht and Moritz Hardt

• Stats 248 Analysis of time-series; by Adityanand Guntuboyina

• EE 223 Stochastic Systems: Estimation and Control, by Venkat Anantharam

• EE 290-02 Advanced topics: High-dimensional statistics for low-dimensional model, by Yi Ma

• EE 290-04 Advanced topics: Population Games, by Murat Arcak

• CS 282A Designing, Visualizing and Understanding Deep Neural Networks, by Sergey Levine

• CS 285 Deep Reinforcement Learning, Decision Making, and Control, by Sergey Levine

Teaching assistant

• IEOR 162: Linear programing and network flow (2023 Spring), by Javad Lavaei

• IEOR 160: Nonlinear and discrete optimization (2019 Fall), by Javad Lavaei