I am a third-year PhD student in Computer Science at Stanford, advised by Professor Aaron Sidford, where I focus on the theoretical foundations of optimization for machine learning. Settings I have worked on include stochastic optimization (learning with noisy gradients) and minimax optimization (robustness to outliers/uncertainty). A highlight is our FOCS 2025 paper that broke a two-decade-old complexity barrier for training linear classifiers and computing Nash equilibria. Outside of research, I enjoy running and playing chess.