Selected Coursework

Optimization Theory

• Convex Optimization (EE 227BT), by Professors Somayeh Sojoudi and Laurent El Ghaoui.

• Convex Optimization and Approximation (EE 227C), by Professor Martin Wainwright.

Machine Learning and Deep Learning

Graduate

• Statistical Learning Theory (CS 281A), by Professors Ben Recht and Moritz Hardt.

• Designing, Visualizing and Understanding Deep Neural Networks (CS 282A), by Dr. Marvin Zhang.

• Deep Reinforcement Learning (CS 285), by Professor Sergey Levine.

• Vision and Language AI Seminar, by Professor Trevor Darrell.

• Natural Language Processing (CS 288), by Professor Dan Klein (audit, not for credit).

Undergraduate

• Machine Learning (CS 4641), by Dr. Xin Chen.

• Computer Vision (CS 4476), by Professor Judy Hoffman.

Control

• Experiential Advanced Control Design I (model predictive control), by Professor Francesco Borrelli.

• Experiential Advanced Control Design II (Kalman filter), by Professor Mark Mueller.

• Advanced Control Theory I (canonical state-space representation forms, Lyapunov stability, LQR control), by Professor Masayoshi Tomizuka.

• Advanced Control Theory II (fundamental trade-offs in controls), by Professor Koushil Sreenath.

• Control System Design (linear systems and introduction to state-space control), by Professor David G Taylor.

Statistics

• Theoretical Statistics I, by Professor Will Fithian.

Game Theory

• Population Games, by Professor Murat Arcak.

Automotive Engineering

• Automotive Engineering, by Professor Michael J. Leamy.

Music

• Singing (Voice Class), by Dr. Candace Johnson.

• Introduction to Music Theory, Analysis, and Notation, by Dr. Robert Yamasato.