- Ph.D., Computational and Mathematical Engineering, Stanford University, 2015
- B.Sc., Mathematics, Duke University, 2010
Jason Lee received his Ph.D. at Stanford University, advised by Trevor Hastie and Jonathan Taylor, in 2015. Before joining Princeton, he was a postdoctoral scholar at UC Berkeley with Michael I. Jordan. His research interests are in machine learning, optimization, and statistics. Lately, he has worked on the foundations of deep learning, non-convex optimization, and reinforcement learning.
Jason D. Lee, Max Simchowitz, Michael I Jordan, and Benjamin Recht. Gradient Descent Converges to Minimizers. Conference on Learning Theory (COLT), 2016.
Rong Ge, Jason D. Lee, and Tengyu Ma. Matrix Completion has No Spurious Local Minimum. Neural Information Processing Systems (NIPS), 2016.
Simon S Du, Jason D Lee, Haochuan Li, Liwei Wang, and Xiyu Zhai. Gradient Descent Finds Global Minima of Deep Neural Networks. International Conference on Machine Learning (ICML), 2019.
Alekh Agarwal, Sham M Kakade, Jason D Lee, and Gaurav Mahajan. On the Theory of Policy Gradient Methods. Journal on Machine Learning Research (short version at COLT), 2020.
Simon S Du, Sham M Kakade, Jason D Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, and Ruosong Wang. Bilinear Classes: A Structural Framework for Provable Generalization in RL. International Conference on Machine Learning (ICML), 2021.
Honors and Awards:
- NSF Career Award
- ONR Young Investigator Award
- Sloan Research Fellowship in Computer Science
- NIPS Best Student Paper Award