Alex Lew, PhD

Alex Lew's research aims to automate and scale up principled probabilistic reasoning, drawing on techniques from programming languages, machine learning, Bayesian statistics, and cognitive science. A key focus is the theory, design, and implementation of probabilistic and differentiable programming languages, which extend traditional programming languages with constructs for optimization and inference over models defined as programs. Lew's work involves both building these languages and using them to experiment with new, probabilistic approaches to engineering more trustworthy intelligent software. He is also a member of the GenLM consortium, a multi-university partnership aiming to better control, compose, and understand language models using the probabilistic programming and Bayesian inference toolkits.

program analysis ;  simulation ;  statistical methods ;  language models  

algorithms ;  artificial intelligence ;  cognition ;  machine learning ;  natural language processing  

Lew joined the Yale Computer Science department as an assistant professor in July 2025. He obtained his PhD from MIT, where he was a member of the Probabilistic Computing Project, co-advised by Vikash Mansinghka and Josh Tenenbaum. Before his PhD, he taught high-school computer science at Commonwealth School in Boston.