Coursework

This list consists of my coursework taken at UC Berkeley, MIT and USC.

Graduate Level Coursework

Computer Science

• • 6.5940 (TinyML and Efficient Deep Learning Computing)By Professor Song Han
• • 6.7920 (Reinforcement Learning: Foundations and Methods)By Professor Cathy Wu & Professor Munther Dahleh
• • 6.8200 (Sensorimotor Learning)By Professor Pulkit Agrawal
• • 6.8300 (Advances in Computer Vision)By Professor Bill Freeman & Professor Vincent Sitzmann & Professor Mina Konaković Luković
• • 15.095 (Machine Learning via a Modern Optimization Lens)By Professor Dimitris Bertsimas
• • 15.622 (Law of AI)By Professor John L. Akula
• • 17.835 (Machine Learning in Politics)By Professor In Song Kim
• • CSCI 699-30165 (Probabilistic and Generative Models)By Professor Willi Neiswanger
• • CSCI 699-30132 (Trustworthy Large Foundation Models)By Professor Jieyu Zhao

Operations Research

• • 6.7830 (Bayesian Modeling and Inference)By Professor Tamara Broderick
• • 6.C57 (Optimization)By Professor Alexandre Jacquillat & Professor Dimitris Bertsimas
• • 6.7220 (Nonlinear Optimization)By Professor Shuvomoy Das Gupta
• • 15.072 (Advanced Analytics Edge)By Professor Bart Van Parys
• • 15.572 (Analytics Lab, Machine Learning, Digital Economy)By Professor Sinan Aral & Professor Abdullah Almaatouq
• • 15.094 (Robust Modeling, Optimization & Computation)By Professor Dimitris Bertsimas
• • 15.873 (System Dynamics)By Professor Johan Chu
• • DSO 677 (Dynamic Optimization and Reinforcement Learning)By Professor Chamsi Hssaine

Mathematics

• • Math 221 (Advanced Matrix Computations/Numerical Algebra)By Professor James Demmel
• • Math 525A (Real Analysis and Measure Theory)By Professor Nicolai Haydn
• • Math 525B (Functional Analysis)By Professor Nicolai Haydn
• • CE 513 (Game Theory for Interactive Autonomy)By Professor Ruolin Li

Electrical Engineering

• • EE 585 (Linear Systems Theory)By Professor Mihailo Jovanovic
• • EE 562 (Random Processes in Engineering)By Professor Urbashi Mitra
• • EE 660 (Machine Learning II: Mathematical Foundations and Methods)By Professor Stephen Tu
• • EE 588 (Optimization for the Information and Data Sciences)By Professor Mahdi Soltanolkotabi

Undergraduate Level Coursework

Computer Science

• • CS 61A (The Structure and Interpretation of Computer Programs)By Professor John DeNero
• • CS 61B (Data Structures)By Professor Josh Hug
• • CS 61C (Great Ideas of Computer Architecture)By Professor Dan Garcia
• • CS 70 (Discrete Mathematics and Probability Theory)By Professor David Wagner
• • CS 170 (Efficient Algorithms and Intractable Problems)By Professor Alessandro Chiesa & Professor Jelani Nelson
• • CS 188 (Introduction to Artificial Intelligence)By Professor Stuart Russell
• • CS 282A (Deep Neural Networks)By Professor Pieter Abbeel
• • CS 285 (Deep Reinforcement Learning)By Professor Sergey Levine
• • CS 289A (Machine Learning)By Professor Michael I. Jordan
• • INFO 259 (Natural Language Processing)By Professor Dan Klein

Mathematics

• • Math 53 (Multivariable Calculus)By Professor James Sethian
• • Math 54 (Linear Algebra and Differential Equations)By Professor Nikhil Srivastava
• • Math 55 (Discrete Math)By Professor Ken Ribet
• • Math 104 (Introduction to Analysis)By Professor Dmitry Vaintrob
• • Math 110 (Linear Algebra)By Professor Edward Frenkel
• • Math 113 (Introduction to Abstract Algebra)By Professor Paul Vojta
• • Math 115 (Introduction to Number Theory)By Professor Richard Borcherds
• • Math 124 (Programming for Mathematical Applications)By Professor Per-Olof Persson
• • Math 130 (Classical Geometries)By Professor James I. Conway
• • Math 185 (Complex Analysis)By Professor John Lott
• • Math 191 (Putnam Workshop)By Professor Alexander Givental
• • MAT 215 (Single Variable Analysis with an Introduction to Proofs)By Professor James I. Conway

Statistics

• • Data 8 (The Foundations of Data Science)By Professor Ani Adhikari
• • Stat 33B (Introduction to Advanced Programming in R)By Professor Deborah Nolan
• • Data 100 (Principles and Techniques of Data Science)By Professor Joseph E. Gonzalez
• • Stat 134 (Concepts of Probability)By Professor Adam Lucas
• • Stat 135 (Concepts of Statistics)By Professor Noureddine El Karoui
• • Stat 150 (Stochastic Processes)By Professor Brett T. Kolesnik
• • Stat 151A (Linear Modelling, Theory and Applications)By Professor Gaston Sanchez
• • Stat 155 (Game Theory)By Professor Shobhana Stoyanov
• • Data H195 (Senior Honors Thesis)By Professor Eric Van Dusen

Electrical Engineering

• • Engin 7 (Computer Programming for Scientists and Engineers)By Professor Mohammad-reza Alam
• • EE 16A (Designing Information Devices and Systems I)By Professor Alexandre Bayen
• • EE 16B (Designing Information Devices and Systems II)By Professor Murat Arcak
• • EE 120 (Signals and Systems)By Professor Babak Ayazifar
• • EE 126 (Probability and Random Processes)By Professor Anant Sahai
• • EE 227A (Optimization Models in Engineering)By Professor Laurent El Ghaoui
• • EE 482 (Linear Control Systems)By Professor Richard Chiang