Yuriy Brun
Yuriy Brun has 2 papers in our library, in 2022–2023; often with Emily First, Talia Ringer, Alex Sanchez-Stern.
OpenAlex · ORCID · Wikidata · h-index 43 · 165 works · 8,204 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 90 times in our library · ⭐ Paper Stars ranking: #6619
Affiliations
🎓University of Massachusetts AmherstAmherst Center ·
United States2012–2026 · 89 papersEmployment 2012–2017 · Assistant Professor, College of Information and Computer SciencesEmployment 2017–2021 · Associate Professor, College of Information and Computer SciencesEmployment since 2021 · Professor, Manning College of Information and Computer Sciences🎓University of WashingtonSeattle ·
United States2009–2015 · 25 papersEmployment 2009–2012 · NSF CRA Postdoctoral Computing Innovation Fellow, Computer Science & Engineering🎓University of Southern CaliforniaLos Angeles ·
United States2004–2011 · 24 papersEducation until 2008 · PhD, Computer Science Department🎓Massachusetts Institute of TechnologyCambridge ·
United States2003Education until 2003 · MEng, Electrical Engineering and Computer ScienceEducation until 2003 · BS, MathematicsEducation until 2003 · BS, Electrical Engineering and Computer Science
Fields, topics and concepts
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputational Theory and Mathematics · 2 worksSoftware · 2 works
Works by year
2020s
Papers
- Baldur: Whole-Proof Generation and Repair with Large Language Models (2023 · 175 citations)
Writing machine-checked proofs for software verification is very costly. Earlier neural provers reduce this by predicting one tactic at a time and searching over proof states, which requires constant back-and-forth between a GPU-hosted model and a CPU-bound proof assistant. Baldur takes a simpler route. It fine-tunes… - Passport: Improving Automated Formal Verification Using Identifiers (2022 · 35 citations)
Machine-learning proof-synthesis tools for the Coq proof assistant learn from existing proof corpora to suggest proof scripts.
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