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Jason Lee is an Associate Professor in the Department of Statistics at the University of California, Berkeley. He previously served as an Associate Professor at Princeton University and has also engaged in research at Google DeepMind. Jason earned his PhD from Stanford University, where he was advised by Trevor Hastie and Jonathan Taylor. Following his doctorate, he became a postdoctoral scholar at UC Berkeley under the guidance of Michael Jordan. His research interests encompass the theoretical underpinnings of machine learning, optimization, and statistics. Recently, he has focused on the foundations of deep learning, representation learning, and reinforcement learning. Jason has received numerous accolades for his contributions to the field, including the Samsung AI Researcher of the Year Award, the National Science Foundation Career Award, and the Office of Naval Research Young Investigator Award. He is also a recipient of the Sloan Research Fellowship and was a finalist for the NeurIPS Student Paper Award.
The Mathematics Subject GRE is required for the Fall 2026 admissions cycle. General GRE is optional.