Welcome

I am Alex (Oleksandr) Polozov, a senior researcher in the Deep Learning group at Microsoft Research, Redmond. I work on neural program synthesis from input-output examples and natural language, intersections of machine learning and software engineering, and neuro-symbolic reasoning. I am particularly interested in combining neural and symbolic techniques to tackle the next generation of AI problems, including program synthesis, planning, and question answering.

My main passion of the last several years has been PROSE, a program synthesis framework for mass-market development of by-example technologies. For details on other projects, please see my CV and the list of publications.

I completed my Ph.D. in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. My advisors were Sumit Gulwani and Zoran Popović. Before joining UW, I received my B.S. in System Analysis with honors from the National Technical University of Ukraine “Kyiv Polytechnic Institute” in 2012.

Latest news

March 2021: “Structure-Grounded Pretraining for Text-to-SQL” will (virtually) appear at NAACL’21.
January 2021: “SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing” will (virtually) appear at ICLR’21.
December 2020: “SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing” and “Learning to Infer Run-Time Invariants from Source Code” presented at the Computer-Assisted Programming (CAP) workshop at NeurIPS 2020.
October 2020: “Structure-Grounded Pretraining for Text-to-SQL” released on arXiv.
July 2020: I gave a talk “Neuro-Symbolic Program Synthesis from Natural Language and Demonstrations” at the 9th Workshop on Synthesis (SYNT).
June 2020: “Neuro-Symbolic Visual Reasoning: Disentangling “Visual” from “Reasoning”” will (virtually) appear at ICML’20.
April 2020: “Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context” will (virtually) appear at ACL’20.
November 2019: “RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers” released on arXiv.
September 2019: I gave a talk “From Examples to Natural Language and Back” at the “State of the Art in Program Synthesis” workshop hosted by Synthetic Minds.; “Program Synthesis and Semantic Parsing with Learned Code Idioms” to appear at NeurIPS’19.
July 2019: I gave a talk on “Program Understanding, Synthesis, and Verification with Graph Neural Networks” at the Learning & Reasoning with Graph-Structured Representations workshop at ICML 2019. Talk recording and slides are available online.
June 2019: “Program Synthesis and Semantic Parsing with Learned Code Idioms” released on arXiv.
May 2019: At ICLR 2019 in New Orleans, we presented our recent work on generative code modeling with GNNs. Also, Gustavo Soares and I showed a first public demo of a our new tool for automating repetitive source code editing on the fly, powered by the PROSE framework.
March 2019: “Are My Invariants Valid? A Learning Approach” released on arXiv.
December 2018: “Generative Code Modeling with Graphs” to appear at ICLR’19.
September 2018: Our new neuro-symbolic technique, execution-guided decoding, has helped two Microsoft Research models to take the top two spots on the WikiSQL leaderboard!; “IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles” released on arXiv.; “Robust Text-to-SQL Generation with Execution-Guided Decoding” released on arXiv.
July 2018: New blog post: “Program Synthesis in 2017-18”.
June 2018: “Execution-Guided Neural Program Decoding” to appear at NAMPI’18.; FlashProfile to appear at OOPSLA’18.; New site layout.
May 2018: “Generative Code Modeling with Graphs” released on arXiv.
April 2018: I will be attending ICLR 2018 in Vancouver to present our work on neural-guided deductive search. Let me know if you want to meet up!
February 2018: Presented “Program Synthesis via Neural-Guided Deductive Search” at the Machine Learning + Programming Languages Workshop at UW.; Neural-Guided Deductive Search to appear at ICLR’18.