S
Sanjit A. Seshia
Researcher at University of California, Berkeley
Publications - 371
Citations - 16942
Sanjit A. Seshia is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Formal verification & Computer science. The author has an hindex of 62, co-authored 344 publications receiving 13964 citations. Previous affiliations of Sanjit A. Seshia include Hebrew University of Jerusalem & Carnegie Mellon University.
Papers
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Book
Introduction to Embedded Systems - A Cyber-Physical Systems Approach
Edward A. Lee,Sanjit A. Seshia +1 more
TL;DR: This book takes a cyber-physical approach to embedded systems, introducing the engineering concepts underlying embedded systems as a technology and as a subject of study.
Proceedings ArticleDOI
Semantics-aware malware detection
TL;DR: Experimental evaluation demonstrates that the malware-detection algorithm can detect variants of malware with a relatively low run-time overhead and the semantics-aware malware detection algorithm is resilient to common obfuscations used by hackers.
Proceedings ArticleDOI
Combinatorial sketching for finite programs
TL;DR: SKETCH is a language for finite programs with linguistic support for sketching and its combinatorial synthesizer is complete for the class of finite programs, guaranteed to complete any sketch in theory, and in practice has scaled to realistic programming problems.
Syntax-guided synthesis
Rajeev Alur,Rastislav Bodik,Garvit Juniwal,Milo M. K. Martin,Mukund Raghothaman,Sanjit A. Seshia,Rishabh Singh,Emina Torlak,Abhishek Udupa,Armando Solar-Lezama +9 more
TL;DR: This work describes three different instantiations of the counter-example-guided-inductive-synthesis (CEGIS) strategy for solving the synthesis problem, reports on prototype implementations, and presents experimental results on an initial set of benchmarks.
Proceedings ArticleDOI
Oracle-guided component-based program synthesis
TL;DR: A novel approach to automatic synthesis of loop-free programs based on a combination of oracle-guided learning from examples, and constraint-based synthesis from components using satisfiability modulo theories (SMT) solvers is presented.