I’m a second-year PhD student at MIT’s Probabilistic Computing Project, co-advised by Vikash Mansinghka and Josh Tenenbaum. 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 mathematics in 2015.
I’m interested in designing systems and abstractions that help people apply, invent, and reason about sophisticated algorithms for probabilistic modeling and inference. I’m also interested in using those systems to explore ideas in knowledge representation, expert systems, logic, and program synthesis.
Marco Cusumano-Towner, Feras Saad, Alexander Lew, Vikash Mansinghka
We introduce Gen, a new probabilistic programming system that outperforms state-of-the-art systems, sometimes by orders of magnitude, on diverse problems including object tracking, estimating 3D body pose from depth images, and inferring the structure of a time series.
Tom Silver, Kelsey Allen, Alexander Lew, Leslie Kaelbling, Josh Tenenbaum
We define an expressive class of program-based policies, and derive an approximate Bayesian inference algorithm for learning them from demonstrations. In five strategy games played on a 2D grid, after just a handful demonstrations, the inferred policies generalize to new game instances that differ substantially from the demonstrations.
As engineers, we love to automate tedious tasks, but when the task at hand requires common sense, automation can be difficult. Probabilistic scripting is a style of declarative programming, based on probabilistic programming, that lets us replace heuristics with principled common-sense reasoning.
A ten-minute introduction to Gen, and some thoughts on how it compares to other probabilistic programming systems.
At MIT, I co-taught a January-term course on applied probabilistic programming, and last spring, served as a TA for 6.885: Probabilistic Programming & AI.
From 2015 to 2018, I taught computer science full-time at Commonwealth School.
In this hands-on introduction to computer science using Racket and Python, students create dozens of interactive programs while exploring core ideas in computation like data representation, abstraction, and recursion.
Students explore big ideas in computing, from machine learning to networking to cryptocurrencies, by coding up the relevant algorithms themselves.
We take a deep dive under the covers to see how lists, trees, and arrays really work. Then we practice algorithmic thinking and algorithm design.
This course is different each year depending on student interest.
Every summer, I serve as a TA at the Duke Machine Learning Summer School.
A slow and thorough introduction to TensorFlow from the ground up, designed for use at the Duke Machine Learning Summer School. Rather than jump straight into building a neural net, we focus first on the fundamentals: graphs, operations, tensors, placeholders, variables, and sessions.
A fun little word game powered by machine learning. With a hundred thousand words to choose from, can you and the robot converge to the same one?
Email me at alexlew AT mit DOT edu.