coursework

Graduate Courses: I’ve taken six graduate courses.

Stat 260: Theoretical Statistics: Additional Chapters, Fall 2024, taught by Prof. Nikita Zhivotovskiy

· Stein's unbiased risk estimate and its applications.

· Minimax lower bounds.

· RKHS theory and its relation to statistics.

· Sparse recovery.

· Elements of sampling theory.

· Analysis of interpolating estimators.

EE 229: Information Theory and Coding, Fall 2024, taught by Prof. Venkatachalam Anantharam

· Fundamental bounds of Shannon theory and their application.

· Source and channel coding theorems.

· Galois field theory, algebraic error-correction codes.

EE 226A: Random Processes in Systems, Spring 2024, taught by Prof. Anant Sahai

· Measure Theory, limit theorems and convergence.

· Gaussian random variables and processes.

· Linear estimation and time series analysis.

· Discrete and continuous time Markov Chains.

· Poisson process.

· Martingales.

Stat 241B: Advanced Topics in Statistical Learning, Spring 2024, taught by Prof. Ryan Tibshirani

· Nearest neighbors and kernels.

· Splines and RKHS methods.

· Minimax theory.

· Empirical process theory.

· Lasso, Ridge and Ridgeless.

· Conformal prediction under distribution shift.

Stat 210A: Theoretical Statistics, Fall 2023, taught by Prof. Will Fithian

· Statistical decision theory (frequentist and Bayesian).

· Exponential families.

· Point estimation.

· Hypothesis testing.

· Resampling methods.

· Estimating equations and maximum likelihood.

· Empirical Bayes.

· Large-sample theory.

· High-dimensional testing, multiple testing and selective inference.

CS 285: Deep Reinforcement Learning, Decision Making, and Control, Fall 2023, taught by Prof. Sergey Levine

· Supervised learning to decision making.

· Q-learning, policy gradients, actor-critic.

· Model-based algorithms: planning, sequence models.

· Exploration.

· Offline reinforcement learning.

· Inverse reinforcement learning.

Others: Linear Algebra; Introductory Mechanics and Relativity; Introduction to Computational Techniques in Physics; Introduction to Abstract Algebra; Introductory Electromagnetism, Waves, and Optics; Data Structures and Programming Methodology; Probability and Random Processes; Introduction to Analysis; Quantum Mechanics; Introduction to Machine Learning; Optimization Models in Engineering; Mathematical Probability Theory; Introduction to Complex Analysis.