معرفی
Dr. Weiming Xiang is an Associate Professor at the School of Computer and Cyber Sciences of Augusta University, Georgia. He transitioned from Assistant Professor (2019–2023) to his current role (2023–present). His academic journey includes postdoctoral positions at Vanderbilt University and University of Texas at Arlington, and a research associate role at the University of Hong Kong. He earned his Ph.D. (2014) in hybrid transportation systems from Southwest Jiaotong University, an M.Sc. (2007) in automation from Nanjing University of Science and Technology, and a B.E. (2005) in electrical engineering from East China Jiaotong University.
Research Interests: Dr. Xiang focuses on formal synthesis and verification techniques for cyber-physical systems (CPS), particularly addressing safety, security, and reliability in learning-enabled CPS. His work spans hybrid systems, neural network verification, control theory, and data-driven modeling, with applications in autonomous vehicles, power systems, and transportation. He develops scalable verification frameworks using reachable set computation, interval arithmetic, and polyhedral methods.
Scientific Awards:
- NSF CAREER Award (2022)
- IEEE Senior Member (2017–present)
- Outstanding Reviewer awards from journals like IEEE Transactions on Automatic Control, Neurocomputing, and Journal of the Franklin Institute
- Top 1% Reviewers in Engineering, Publons (2018)
Grants & Collaborations: Dr. Xiang leads multiple NSF-funded projects, including a $498,985 CAREER Award (2022–2027) for machine-learning-intensive CPS upgrades, a $499,000 CPS Program grant with Jason Orlosky (2022–2025) for XR-assisted h-CPS modeling, and a $270,913 NSF grant with Hoang-Dung Tran (2023–2026) for safety assessment of learning-enabled systems. He also contributes to Augusta University's CyberCorps Scholarships for Service program.
Labs & Teams: Dr. Xiang leads the AI-CPS Lab, which explores intersections between artificial intelligence, formal verification, and cyber-physical systems. His lab collaborates on projects involving runtime safety monitoring, neural network compression, and hybrid control architectures.
