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:

**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.*

- 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.