PhD candidate, MIT Probabilsitic Computing Project*Starting Fall 2025:* Assistant Professor, Yale Computer Science

Starting Fall 2025, I’m thrilled to be joining Yale’s Computer Science department as an Assistant Professor. Please get in touch if you might be interested in a PhD or postdoc on probabilistic and differentiable programming!

**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:

**Theoretical foundations for probabilistic programming:**e.g., new semantic models for reasoning clearly about expressive probabilistic and differentiable programs, a type system for enforcing measure-theoretic correctness properties of inference algorithms, and a unifying framework for designing Monte Carlo and variational inference algorithms with powerful proposal distributions.**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.**High-level tools that exploit this automation for scalable probabilistic reasoning in specific domains:**e.g., a data-cleaning system that accurately and efficiently detects and corrects errors in real-world datasets with millions of records, and a new domain-specific language for steering large language models to behave more reliably.

*Selected publications are listed below; please see Google Scholar for a complete list. Asterisks (*) indicate co-first authorship.*

- McCoy Becker*,
**Alexander Lew***, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin Rinard, Vikash Mansinghka

PLDI 2024 - Mathieu Huot*,
**Alexander Lew***, Vikash Mansinghka, Sam Staton

LICS 2023, distinguished paper **Alexander Lew**, Matin Ghavami, Martin Rinard, Vikash Mansinghka

PLDI 2023**Alexander Lew***, Mathieu Huot*, Sam Staton, Vikash Mansinghka

POPL 2023, distinguished paper**Alexander Lew***, George Matheos*, Tan Zhi-Xuan, Matin Ghavami, Nishad Gothoskar, Stuart Russell, Vikash Mansinghka

AISTATS 2023**Alexander Lew**, Marco Cusumano-Towner, Vikash Mansinghka

UAI 2022**Alexander Lew**, Monica Agrawal, David Sontag, Vikash Mansinghka

AISTATS 2021, oral presentation**Alexander Lew**, Michael Henry Tessler, Vikash Mansinghka, Joshua Tenenbaum

COGSCI 2020**Alexander Lew**, Marco Cusumano-Towner, Benjamin Sherman, Michael Carbin, Vikash Mansinghka

POPL 2020- Tom Silver, Kelsey Allen,
**Alexander Lew**, Leslie Kaelbling, Josh Tenenbaum

AAAI 2020 - Marco Cusumano-Towner, Feras Saad,
**Alexander Lew**, Vikash Mansinghka

PLDI 2019

Some of my research talks are available online with video recordings:

- Integrating Language into Intelligent Architectures (with Lio Wong)2023-08-17 /
*Simons Institute Workshop on Large Language Models and Transformers* - 2023-06-20 /
*PLDI 2023* - 2023-01-19 /
*POPL 2023* - 2023-01-15 /
*Languages for Inference Workshop 2023* - 2022-01-16 /
*Languages for Inference Workshop 2022* - Reasoning about AD in Higher-Order, Recursive, Probabilistic Languages (best talk award)2021-12-13 /
*NeurIPS Differentiable Programming Workshop 2021* - 2021-04-14 /
*AISTATS 2021* - 2021-02-18 /
*Joint PPS-PIHOC-DIAPASoN Workshop 2021* - 2021-01-17 /
*Languages for Inference Workshop 2021* - 2020-08-03 /
*COGSCI 2020* - 2020-01-23 /
*POPL 2020* - 2019-09-14 /
*StrangeLoop 2019* - Gen: A General-Purpose Probabilistic Programming System (delivered in Marco Cusumano-Towner’s stead)2019-07-25 /
*JuliaCon 2019*

**At MIT**, I co-taught a January-term course on applied probabilistic programming, and served twice as a TA for 6.885: Probabilistic Programming & AI.

**From 2015 to 2018**, I taught computer science full-time at Commonwealth School.

- CS 1: Intro to Program Design
- CS 2: AP Computer Science Principles
- CS 3: Data Structures and Algorithms
- CS 4: Advanced Topics in Computer Science

**From 2015 to 2019**, I served as a TA at the Duke Machine Learning Summer School.

Email me at alexlew AT mit DOT edu.