Jason Lee is an assistant professor in Electrical Engineering and Computer Science at Princeton University. Prior to that, he was in the Data Science and Operations department at the University of Southern California and a postdoctoral researcher at UC Berkeley working with Michael Jordan. Jason received his PhD at Stanford University advised by Trevor Hastie and Jonathan Taylor. His research interests are in the theory of machine learning, optimization, and statistics. Lately, he has worked on the foundations of deep learning, non-convex optimization algorithm, and reinforcement learning. He has received the ONR Young Investigator Award in Mathematical Data Science, Sloan Research Fellowship in 2019, NeurIPS Best Student Paper Award and Finalist for the Best Paper Prize for Young Researchers in Continuous Optimization.