I’m a final-year PhD student at MIT’s Probabilistic Computing Project, co-advised by Vikash Mansinghka and Josh Tenenbaum, and supported by an NSF Graduate Research Fellowship. Before coming to MIT, I taught high school computer science at Commonwealth School in Boston. And before that, I was a student at Yale, where I received a B.S. in computer science and math in 2015.
My research aims to automate and scale up principled probabilistic reasoning, in much the same way that tools like TensorFlow and PyTorch have automated and scaled up deep learning. To that end, I develop:
Probabilistic program compilers that automate and speed up the math behind learning and inference: e.g., a new AD algorithm that automatically constructs gradient estimators for optimizing probabilistic objectives, and a new compiler for Gen programs that synthesizes fast, unbiased estimators of probability densities and their reciprocals, for use in Monte Carlo and variational inference.
Selected publications are listed below; please see Google Scholar for a complete list. Asterisks (*) indicate co-first authorship.
Some of my research talks are available online with video recordings:
From 2015 to 2018, I taught computer science full-time at Commonwealth School.
From 2015 to 2019, I served as a TA at the Duke Machine Learning Summer School.
Email me at alexlew AT mit DOT edu.