Alex Lew

Alex Lew is an Assistant Professor of Computer Science at Yale University, affiliated with the Yale School of Engineering & Applied Science. He is a core member of the GenLM consortium, a multi-university initiative focused on controlling and understanding large language models through probabilistic programming and Bayesian inference. His research emphasizes automating and scaling principled probabilistic reasoning, combining techniques from programming languages, machine learning, Bayesian statistics, and cognitive science.

Alex holds a Ph.D. and S.M. from the Massachusetts Institute of Technology (MIT) and a B.S. from Yale University. His work bridges theoretical foundations and practical applications, particularly in probabilistic programming languages and their integration with modern machine learning systems. Key contributions include advances in programmable variational inference, denotational semantics for probabilistic programs, and scalable Bayesian data cleaning.

His research has been recognized with prestigious awards, including the ACM SIGPLAN Distinguished Paper Award (POPL 2023), ACM SIGLOG Distinguished Paper Award (LICS 2023), and the Probability and Programming Research Award from Meta (2023). His publications span top venues like ICLR, PLDI, and POPL, focusing on topics such as sequential Monte Carlo, stochastic inference algorithms, and formal semantics.

Alex collaborates with industry and academia through the GenLM consortium and contributes to open-source probabilistic programming systems like Gen. His lab focuses on advancing the theoretical and practical tools for probabilistic reasoning in complex systems.