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My research is advised by Chelsea Finn and Dorsa Sadigh. I am affiliated with Stanford Artificial Intelligence Laboratory (SAIL).
Previously, I studied mathematical physics as an undergraduate at Stanford University. Outside of AI, I am interested in pure mathematics, especially abstract algebra and neighbouring fields. I am interested in the connections of other seemingly disparate fields of science to our understanding of machine learning.
            
            
(*) denotes co-first authorship
Full list of publications can be found here: Publications.
Introductory notes on various topics I have found to be fascinating and/or useful in my own research.
Category Theory and Algebraic Geometry. Notes on introductory category theory and foundational constructions in algebraic geometry (work in progress).
Abstract Algebra. Notes on abstract algebra (work in progress).
Whitney's Embedding Theorem and Immersion Theorem. Weak versions of Whitney's Embedding and Immersion theorem, which are often sufficient.
Policy Gradient Methods. Building blocks of policy gradient methods in online reinforcement learning.
Trust Region Optimization Methods. Introduction to trust region methods for optimization.
CS 224R - Deep Reinforcement Learning. Head CA. Spring, 2025.
CS 229 - Machine Learning. CA. Winter, 2025.