CAREER: From Neural Network Verification to General SMT Solving: A New Framework for Solvi — NSF Award to University of Illinois a
Satisfiability Modulo Theories (SMT) problems are akin to mathematical puzzles: given a mix of equations and logical rules, an SMT solver determines whether there exists an assignment of variables that satisfies everything at once. SMT solving underpins high-stakes assurance tasks: for example, proving that a self-driv
| Award title | CAREER: From Neural Network Verification to General SMT Solving: A New Framework for Solvi |
|---|---|
| Award ID | 2543005 |
| Awardee | University of Illinois at Urbana-Champaign |
| City | URBANA |
| State | IL |
| Amount obligated | $368,343 |
| Principal investigator | Huan Zhang |
| Program | Software & Hardware Foundation |
| Start date | 08/01/2026 |
| Abstract | Satisfiability Modulo Theories (SMT) problems are akin to mathematical puzzles: given a mix of equations and logical rules, an SMT solver determines whether there exists an assignment of variables that satisfies everything at once. SMT solving underpins high-stakes assurance tasks: for example, proving that a self-driving car cannot steer into an obstacle under modeled operating conditions, and supporting safety checks in domains like avionics, medical devices, and power systems. These problems |
| Source | NSF Awards |
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