Research Bio

Jason D. Lee develops theory for machine learning, optimization, and statistics, with an emphasis on foundations of deep learning and efficient algorithms for large-scale models. His research analyzes the landscape and dynamics of nonconvex optimization, generalization in overparameterized regimes, and the statistical properties of modern training methods. By bridging optimization theory with practical ML systems, Lee helps explain why certain algorithms work and how to design faster, more reliable procedures. His work has influenced understanding of representation learning and reinforcement learning, among other areas.

Lee is an Associate Professor in UC Berkeley’s Departments of Electrical Engineering & Computer Sciences and Statistics. His expertise is the theory of machine learning and optimization, building principled frameworks for modern AI.

Research Expertise and Interest

machine learning theory, optimization, statistics, deep learning