I’m a Machine Learning researcher trying to get computers to automatically write code. I’m working on something new—more details soon!

I recently received a PhD from MIT, where I was advised by Josh Tenenbaum and Armando Solar-Lezama. I also worked with Brenden Lake (NYU/Facebook AI) and Jacob Andreas. My research focus at MIT was program synthesis: using deep learning as well as symbolic techniques to automatically write programs.

I interned at Google Brain, working with Augustus Odena and Charles Sutton. We used very large language models (>100 billion parameters) to write and understand Python programs.

I’ve also spent time as an intern at Facebook AI Research. Before MIT, I studied physics at Harvard.

Updates

• I defended my thesis and graduated from MIT! Thanks so much to everybody I worked with at MIT.
• We posted our new “scratchpad” paper on Arxiv: paper, tweet.
• Summer 2021: Internship at Google Brain with Augustus Odena and Charles Sutton. Check out our paper on program synthesis with large langauge models!
• Our paper Representing Partial Programs with Blended Abstract Semantics was acccepted to ICLR 2021
• Presented Learning Compositional Rules via Neural Program Synthesis at NeurIPS 2020
• Summer 2020: Internship at Facebook AI with Brenden Lake
• Presented Write, Execute, Assess: Program Synthesis with a REPL at NeurIPS 2019
• Our paper Learning to Infer Program Sketches was accepted to ICML 2019 and featured on MIT News

Publications

An up-to-date list may be found at Google Scholar

• Show Your Work: Scratchpads for Intermediate Computation with Language Models
  Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, Augustus Odena.
  2021.

• Program Synthesis with Large Language Models.
  Jacob Austin*, Augustus Odena*, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, Charles Sutton.
  2021. [data]

• Implicit Representations of Meaning in Neural Language Models.
  Belinda Z. Li, Maxwell Nye, Jacob Andreas.
  ACL 2021. [code]

• Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning.
  Maxwell Nye, Michael Henry Tessler, Josh Tenenbaum, Brenden Lake.
  NeurIPS 2021.

• Communicating Natural Programs to Humans and Machines.
  Samuel Acquaviva, Yewen Pu, Marta Kryven, Catherine Wong, Gabrielle E Ecanow, Maxwell Nye, Theodoros Sechopoulos, Michael Henry Tessler, Josh Tenenbaum.
  In submission. [data]

• A Large-Scale Benchmark for Few-Shot Program Induction and Synthesis.
  Ferran Alet*, Javier Lopez-Contreras*, James Koppel, Maxwell Nye, Armando Solar-Lezama, Tomas Lozano-Perez, Leslie Kaelbling, Josh Tenenbaum.
  ICML 2021. Spotlight.

• Representing Partial Programs with Blended Abstract Semantics.
  Maxwell Nye, Yewen Pu, Matthew Bowers, Jacob Andreas, Josh Tenenbaum, Armando Solar-Lezama.
  ICLR 2021. Also presented at NeurIPS 2020 Workshop on Computer-Assisted Programming.

• DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning.
  Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sablé-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Josh Tenenbaum.
  PLDI 2021.

• Learning Compositional Rules via Neural Program Synthesis.
  Maxwell Nye, Armando Solar-Lezama, Josh Tenenbaum, Brenden Lake.
  NeurIPS 2020. [code]
  Also presented at NeurIPS 2019 Workshop on Context and Compositionality as a spotlight talk.

• Write, Execute, Assess: Program Synthesis with a REPL.
  Kevin Ellis*, Maxwell Nye*, Yewen Pu*, Felix Sosa*, Josh Tenenbaum, Armando Solar-Lezama.
  NeurIPS 2019.

• Learning to Infer Program Sketches.
  Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama.
  ICML 2019. [code]
  Press: MIT News

• The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples.
  Luke Hewitt, Maxwell Nye, Andreea Gane, Tommi Jaakkola, Josh Tenenbaum.
  UAI 2018, oral presentation. [code]

• Are Efficient Deep Representations Learnable?
  Maxwell Nye, Andrew Saxe.
  ICLR 2018, workshop.

*equal contribution

Education

• MIT (2017 - 2022)
  PhD. Research Areas: Machine Learning and Program Synthesis
  Advisors: Josh Tenenbaum and Armando Solar-Lezama
• Harvard University (2013 - 2017)
  BA, Physics
  Advisor: Haim Sompolinsky