Holden Lee, an assistant professor in the Department of Applied Mathematics and Statistics, explores the interplay between machine learning, probability, and theoretical computer science. His research focuses on building theoretical foundations for probabilistic methods in modern machine learning with a view towards designing more efficient and reliable algorithms. This includes understanding the success and shortcomings of deep learning-based generative models, as well as proving convergence guarantees for sampling (Markov Chain Monte Carlo) algorithms, especially beyond the “log-concave” setting where classical theory applies. He has also worked on algorithms for prediction and control of dynamical systems from a learning-theoretic perspective.

Lee joined Johns Hopkins in fall 2022 after a postdoc appointment at Duke University. He was a Simons fellow at UC Berkeley in fall 2021. He received his bachelor’s degree from MIT and PhD from Princeton, both in mathematics.