Research

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, an expressive discrete probabilistic programming language called Pluck that exploits laziness for efficient inference, 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.

PLaYa Lab

Our research group is the Probabilistic Languages at Yale (PLaYa) Lab.

Selected Publications

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

• GradInf: Gradient Estimation as Probabilistic Inference
  
  Gaurav Arya, Mathieu Huot, Moritz Schauer, Alexander Lew, Feras A Saad
  PLDI 2026
• Incremental Computation for Efficient Programmable Inference in Probabilistic Programs
  
  Fabian Zaiser, Jack Czenszak, Martin Rinard, Vikash Mansinghka, Alexander Lew
  PLDI 2026
• Probabilistic Programming with Vectorized Programmable Inference
  
  McCoy Becker, Mathieu Huot, George Matheos, Xiaoyan Wang, Karen Chung, Colin Smith, Sam Ritchie, Rif A Saurous, Alexander Lew, Martin Rinard, Vikash Mansinghka
  POPL 2026
• Fast Controlled Generation from Language Models via Adaptive Weighted Rejection Sampling
  
  Benjamin Lipkin, Benjamin LeBrun, Jacob Hoover Vigly, João Loula, David R MacIver, Li Du, Jason Eisner, Ryan Cotterell, Vikash Mansinghka, Timothy J O'Donnell^†, Alexander Lew^†, Tim Vieira^†
  COLM 2025, outstanding paper
• Self-Steering Language Models
  
  Gabriel Grand, Joshua B Tenenbaum, Vikash Mansinghka, Alexander Lew, Jacob Andreas
  COLM 2025
• Stochastic Lazy Knowledge Compilation for Inference in Discrete Probabilistic Programs
  
  Maddy Bowers*, Alexander Lew*, Joshua B Tenenbaum, Armando Solar-Lezama, Vikash K Mansinghka
  PLDI 2025
• Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo
  
  João Loula*, Benjamin LeBrun*, Li Du, Ben Lipkin, Clemente Pasti, Gabriel Grand, Tianyu Liu, Yahya Emara, Marjorie Freedman, Jason Eisner, Ryan Cotterell, Vikash Mansinghka^†, Alexander Lew^†, Tim Vieira^†, Timothy J O'Donnell^†
  ICLR 2025, selected for oral presentation
• Automatic Integration and Differentiation of Probabilistic Programs
  
  Alexander Lew
  MIT PhD Thesis 2025, Sprowls thesis award
• Probabilistic Programming with Programmable Variational Inference
  
  McCoy Becker*, Alexander Lew*, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin Rinard, Vikash Mansinghka
  PLDI 2024
• ωPAP Semantics: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs
  
  Mathieu Huot*, Alexander Lew*, Vikash Mansinghka, Sam Staton
  LICS 2023, distinguished paper
• Probabilistic Programming with Stochastic Probabilities
  
  Alexander Lew, Matin Ghavami, Martin Rinard, Vikash Mansinghka
  PLDI 2023
• ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs
  
  Alexander Lew*, Mathieu Huot*, Sam Staton, Vikash Mansinghka
  POPL 2023, distinguished paper
• SMCP³: Sequential Monte Carlo with Probabilistic Program Proposals
  
  Alexander Lew*, George Matheos*, Tan Zhi-Xuan, Matin Ghavami, Nishad Gothoskar, Stuart Russell, Vikash Mansinghka
  AISTATS 2023
• Recursive Monte Carlo and Variational Inference with Auxiliary Variables
  
  Alexander Lew, Marco Cusumano-Towner, Vikash Mansinghka
  UAI 2022
• PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
  
  Alexander Lew, Monica Agrawal, David Sontag, Vikash Mansinghka
  AISTATS 2021, selected for oral presentation
• Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought
  
  Alexander Lew, Michael Henry Tessler, Vikash Mansinghka, Joshua Tenenbaum
  COGSCI 2020
• Trace Types and Denotational Semantics for Sound Programmable Inference in Probabilistic Languages
  
  Alexander Lew, Marco Cusumano-Towner, Benjamin Sherman, Michael Carbin, Vikash Mansinghka
  POPL 2020
• Few-Shot Bayesian Imitation Learning with Logical Program Policies
  
  Tom Silver, Kelsey Allen, Alexander Lew, Leslie Kaelbling, Josh Tenenbaum
  AAAI 2020
• Gen: A General-Purpose Probabilistic Programming System with Programmable Inference
  
  Marco Cusumano-Towner, Feras Saad, Alexander Lew, Vikash Mansinghka
  PLDI 2019

Recorded Talks

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

• Automatic Integration and Differentiation of Probabilistic Programs
  
  2026-01-21 / Yale Foundations of Data Science Colloquium
• Scaling Probabilistic AI via Automatic Differentiation of Probabilistic Programs
  
  2024-03-25 / University of Wisconsin - Weekly PL Seminar
• Integrating Language into Intelligent Architectures (with Lio Wong)
  
  2023-08-17 / Simons Institute Workshop on Large Language Models and Transformers
• Probabilistic Programming with Stochastic Probabilities
  
  2023-06-20 / PLDI 2023
• ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs
  
  2023-01-19 / POPL 2023
• What do the posteriors of probabilistic programs look like?
  
  2023-01-15 / Languages for Inference Workshop 2023
• Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages
  
  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
• PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
  
  2021-04-14 / AISTATS 2021
• Probabilistic Programming with Correct-by-Construction Densities
  
  2021-02-18 / Joint PPS-PIHOC-DIAPASoN Workshop 2021
• On Automating the Estimation of Radon-Nikodym Derivatives for Probabilistic Programs
  
  2021-01-17 / Languages for Inference Workshop 2021
• Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought
  
  2020-08-03 / COGSCI 2020
• Trace Types and Denotational Semantics for Sound Programmable Inference in Probabilistic Languages
  
  2020-01-23 / POPL 2020
• Probabilistic Scripts for Automating Common-Sense Tasks
  
  2019-09-14 / StrangeLoop 2019
• Gen: A General-Purpose Probabilistic Programming System (delivered in Marco Cusumano-Towner’s stead)
  
  2019-07-25 / JuliaCon 2019

Teaching

At Yale, I am teaching CPSC 5585: Probabilistic Programming in Fall 2026. I have previously taught CPSC 2010: Introduction to Computer Science (Spring 2026) and a graduate seminar, CPSC 7260: Differentiable and Probabilistic Programming Languages (Fall 2025).

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

Contact

Email me at alexander DOT lew AT yale DOT edu.