Jason Lee
About
Jason Lee is an associate professor in EECS and Statistics at UC Berkeley. Prior to that, he was an associate professor at Princeton and a researcher at Google Deepmind. Jason received his PhD at Stanford University advised by Trevor Hastie and Jonathan Taylor, and was a postdoctoral scholar at UC Berkeley advised by Michael Jordan. His research interests are in the theory of machine learning, optimization, and statistics. Lately, he has worked on the foundations of deep learning, representation learning, and reinforcement learning. He has received the Samsung AI Researcher of the Year Award, NSF Career Award, ONR Young Investigator Award in Mathematical Data Science, Sloan Research Fellowship, NeurIPS Best Student Paper Award and Finalist for the Best Paper Prize for Young Researchers in Continuous Optimization.
Research Performance Summary
First Recorded Paper
Estimation of intrinsic dimensionality of samples from noisy low-dimensional manifolds in high dimensions with multiscale SVD
Year: 2009
Citations: 79
Venue: 2009 IEEE/SP 15th Workshop on Statistical Signal Processing
Latest Recorded Paper
What One Cannot, Two Can: Two-Layer Transformers Provably Represent Induction Heads on Any-Order Markov Chains
Year: 2025
Citations: 0
Venue: arXiv preprint arXiv:2508.07208
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.