In this talk, I will describe a new approach for building efficient, automated decision procedures for first-order logics involving arithmetic. The central idea is to transform decision problems involving arithmetic to problems in the Boolean domain, such as Boolean satisfiability solving, thereby leveraging recent advances in that area. The transformation automatically detects and exploits problem structure based on new theoretical results and machine learning. The results of experimental evaluations show that our decision procedures can outperform other state-of-the-art procedures by several orders of magnitude.

The decision procedures form the computational engines for two verification systems, UCLID and TMV. These systems have been applied to problems in computer security, electronic design automation, and software engineering that require efficient and precise analysis of system functionality and timing. The talk will briefly mention some of these applications.

Speaker Bio:

Sanjit A. Seshia is an assistant professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received an M.S. and a Ph.D. in Computer Science from Carnegie Mellon University, and a B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Bombay. His research interests are in dependable computing and computational logic, with a current focus on applying automated formal methods to problems in computer security, electronic design automation, and program analysis.

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