Armando Solar-Lezama

About

Armando Solar-Lezama is a Professor of Computing at MIT Schwarzman College of Computing and a faculty member of the EECS department. He holds the title of Distinguished Professor of Computing and is involved in research areas including Programming Languages and Software Engineering, and Systems and Networking. His work focuses on developing innovative computational systems, with particular emphasis on the analysis and synthesis of systems that interact with the external world through perception, communication, and action, while also learning, making decisions, and adapting to changing environments. As a leader in the field, he contributes to advancing the understanding and development of intelligent systems that address complex challenges in computing and engineering.

Research topics

• Computer Science
• Artificial Intelligence
• Programming language
• Machine Learning
• Computational biology
• Data science
• Theoretical computer science
• Biology

Selected publications

• Fusion 360 gallery

  ACM Transactions on Graphics · 2021-07-19 · 153 citations

  articleOpen access

  Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software. Despite the pervasiveness of parametric CAD and a growing interest from the research community, currently there does not exist a dataset of realistic CAD models in a concise programmatic form. In this paper we present the Fusion 360 Gallery , consisting of a simple language with just the sketch and extrude modeling operatio…

  PublisherOA PDFDOI

• DreamCoder: growing generalizable, interpretable knowledge with wake–sleep Bayesian program learning

  Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2023 · 104 citations

  Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages-systems of concepts, alongside the skills to use them. We present DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by creating domain-specific programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages. A 'wake-sleep' lea…

  PublisherOA PDFDOI

• DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning

  2021 · 92 citations

  We present a system for inductive program synthesis called DreamCoder, which inputs a corpus of synthesis problems each specified by one or a few examples, and automatically derives a library of program components and a neural search policy that can be used to efficiently solve other similar synthesis problems. The library and search policy bootstrap each other iteratively through a variant of "wake-sleep" approximate Bayesian learning. A new refactoring algorithm based on E-graph matching ident…

  DOI

• Neurosymbolic Programming

  Foundations and Trends® in Programming Languages · 2021 · 53 citations

  We survey recent work on neurosymbolic programming, an emerging area that bridges the areas of deep learning and program synthesis. Like in classic machine learning, the goal here is to learn functions from data. However, these functions are represented as programs that can use neural modules in addition to symbolic primitives and are induced using a combination of symbolic search and gradient-based optimization. Neurosymbolic programming can offer multiple advantages over end-to-end deep learni…

  DOI

• LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

  arXiv (Cornell University) · 2024-03-12 · 23 citations

  preprintOpen access

  Large Language Models (LLMs) applied to code-related applications have emerged as a prominent field, attracting significant interest from both academia and industry. However, as new and improved LLMs are developed, existing evaluation benchmarks (e.g., HumanEval, MBPP) are no longer sufficient for assessing their capabilities. In this work, we propose LiveCodeBench, a comprehensive and contamination-free evaluation of LLMs for code, which continuously collects new problems over time from contest…

  PublisherOA PDFDOI

Recent grants

• InTrans: TRI-MIT Collaboration on Formal Verification Meets Big Data Intelligence in the Trillion Miles Challenge

  NSF · $232k · 2017–2019

• SHF: Medium: Collaborative Research: Marrying program analysis and numerical search

  NSF · $600k · 2012–2016

• SHF: Small: Human-Centered Software Synthesis

  NSF · $405k · 2011–2015

Frequent coauthors

• Rishabh Singh

  Texas A&M University

  44 shared

• Rastislav Bodík

  Google (United States)

  40 shared

• Rajeev Alur

  University of Pennsylvania

  31 shared

• Yewen Pu

  29 shared

• Dana Fisman

  Yale University

  29 shared

• Sanjit A. Seshia

  28 shared

• Joshua B. Tenenbaum

  Massachusetts Institute of Technology

  28 shared

• Liviu Tancau

  University of California, Berkeley

  25 shared

Labs

• MIT EECS Artificial Intelligence + Decision-makingPI

Awards & honors

• Inaugural Distinguished Professor of Computing (2023)

Similar researchers at Massachusetts Institute of Technology

• Manolis Kellish-164
• Antonio Torralbah-133
• Daniela Rush-129