Chi Jin is an Assistant Professor of Electrical and Computer Engineering at Princeton University. He obtained his PhD in Computer Science at UC Berkeley, advised by Michael Jordan. He received his BS in Physics from Peking University. His research interest lies in theoretical machine learning, reinforcement learning, optimization and game theory. His representative work includes proving noisy gradient descent / accelerated gradient descent escape saddle points efficiently, proving sample complexity bounds for optimistic Q-learning / Least-squares value iteration, and designing near-optimal algorithms for minimax optimization.