Westley Weimer is a Professor in the Department of Electrical Engineering and Computer Science. He also works to help programmers address defects, understand programs, and program correctly. His research spans automated program repair, formal verification, program improvement, human studies, and language feature design. He received his PhD from Berkeley and now serves as an associate professor at the University of Virginia. His work has led to over 8,400 citations, eight distinguished paper awards, four multi-conference research awards, and one ten-year most influential paper award. He also enjoys fencing and overacting.
Dr. Weimer's current main research interests relate to consciousness, time, and advancing software quality by using both static and dynamic programming language approaches. On the purely-CS side, he is particularly concerned with automatic or minimally-guided techniques that can scale and be applied easily to large, existing programs. He believes that finding bugs is insufficient, and he also works to help programmers address defects, understand programs, and program correctly.
Kevin Angstadt, Jack Wadden, Westley Weimer, Kevin Skadron: Portable Programming with RAPID. IEEE Transactions on Parallel and Distributed Systems. 2018.
Kevin Angstadt, Jack Wadden, Vinh Dang, Ted Xie, Westley Weimer, Mircea R. Stan, Kevin Skadron: MNCaRT: An Open Source, Multi-Architecture Automata-Processing Research and Execution Ecosystem: IEEE Transactions on Parallel and Distributed Systems. 2017.
Claire Le Goues, Yuriy Brun, Stephanie Forrest, Westley Weimer: Clarifications on the Construction and Use of the ManyBugs Benchmark: IEEE Trans. Software Engineering 43(11): 1089-1090 (2017).
Kevin Leach, Fengwei Zhang, Westley Weimer: Scotch: Combining Software Guard Extensions and System Management Mode to Monitor Cloud Resource Usage: Research Attacks, Intrusions, and Defenses (RAID) 2017: 403-424.
Eric Seidel, Huma Sibghat, Kamalika Chauduri, Westley Weimer, Ranjit Jhala: Learning to Blame: Localizing Student Type Errors with Data-Driven Diagnosis: Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA) 2017: 60:1-60:27.