معرفی
Stanley Bak is an Assistant Professor in the Department of Computer Science at Stony Brook University, where he has been a faculty member since Fall 2020. His research focuses on developing practical formal methods for verifying safety-critical autonomous systems, with emphasis on scalability and real-world applicability in cyber-physical and neural network domains.
His educational background includes:
- Bachelor's in Computer Science from Rensselaer Polytechnic Institute (2007, summa cum laude)
- Master's in Computer Science from University of Illinois at Urbana-Champaign (2009)
- Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2013)
Dr. Bak's research centers on formal verification of cyber-physical systems and neural networks, developing theoretical foundations, efficient tools, and experimental systems to ensure safety in autonomy. His work bridges abstract mathematical methods with practical engineering challenges, particularly in aerospace and autonomous vehicle applications where failure is unacceptable.
Analysis of his 2023-2025 publications reveals dominant trends in scalable verification techniques using polynomial zonotopes for reachability analysis, Koopman operator linearization for nonlinear systems, and abstract domain development (hexatope/octatope) for neural networks. His contributions to the VNN-COMP competitions demonstrate leadership in benchmarking neural network verification tools.
His scientific recognition includes:
- Founders Award of Excellence (2004) for undergraduate research at RPI
- Debra and Ira Cohen Graduate Fellowship (2008, 2009) from UIUC
- SMART Scholarship (2009-2013)
Dr. Bak leads the CAREER-funded project "Verified AI in Cyber-Physical Systems through Input Quantization" (2023) and previously co-founded Safe Sky Analytics for verification consulting. His teaching includes graduate courses CSE 510 and CSE 643, and he actively mentors students in formal methods research. Prior to Stony Brook, he served as a Research Computer Scientist at the US Air Force Research Lab (2013-2018) and taught at Georgetown University.
His work integrates with Stony Brook's AI Innovation Institute, focusing on verification methodologies for autonomous systems where theoretical guarantees meet real-world deployment requirements.
