Get the latest news
Subscribe to receive updates from the School of Data Science.
Faculty
Peter Beling
Professor of Data Science
School of Data Science
Additional Roles
Director
National Security Data and Policy Institute
Email:
beling@virginia.edu
Phone: (434) 989-8733
Peter A. Beling is a professor at the University of Virginia School of Data Science, where he focuses on generative AI research as part of UVA’s new National Security Data and Policy Institute. His work centers on resilient, mission-aligned AI, with broader interests in cyber resilience and trusted systems. At the nexus of systems theory and artificial intelligence, Beling explores large language models, reinforcement learning, and the co-design of algorithms and the systems in which they operate. His research supports a range of national security applications, including mission engineering, test and evaluation, and predictive maintenance.
From 2021 to 2025, Beling was on the faculty of Virginia Tech, where he served as director of the Intelligent Systems Division at the National Security Institute and as a professor in the Grado Department of Industrial and Systems Engineering. He also contributed to the Research Council of the Systems Engineering Research Center (SERC), a Department of Defense University Affiliated Research Center (UARC). From 1993 to 2021, he held a series of roles at UVA, including professor of Systems Engineering, director of the UVA site of the Center for Visual and Decision Informatics (a National Science Foundation Industry/University Cooperative Research Center), and director of the Adaptive Decision Systems Laboratory.
Beling holds a Ph.D. in operations research from the University of California, Berkeley, an M.S. in operations research from The George Washington University, and a B.S. in mathematics from UVA.
Education
- Ph.D., University of California at Berkeley, Industrial Engineering and Operations Research, December 1991
- M.S., George Washington University, Operations Research, May 1987
- B.A., University of Virginia, Mathematics, December 1984
Selected Publications
Shadab, N., Cody, T., Salado, A., Beling, P., ‘’A Systems-theoretical Formalization of Closed Systems,” IEEE Open Journal of Systems Engineering, pp. 26–37, 2024.
Cody, T. and Beling, P., “A Systems Theory of Transfer Learning,” IEEE Systems Journal, 17 (1), pp. 26–37, 2023.
Cody, T., Adams, S., and Beling, P., “Empirically Measuring Transfer Distance for System Design and Operation,” IEEE Systems Journal, 17(1), pp. 26–37, 2023.
Adams, S., Cody, T., and Beling, P., “A Survey of Inverse Reinforcement Learning,” Artificial Intelligence Review, pp. 1–40, 2022.
Su, J., Huang, J., Adams, S., Chang, Q., and Beling, P., “Deep Multi-agent Reinforcement Learning for Multi-level Preventive Maintenance in Manufacturing Systems,” Expert Systems with Applications, 192, 116323, 2022.
Bakirtzis, G., Sherburne, T., Adams, S., Horowitz, B., Beling, P., and Fleming, C., “An Ontological Metamodel for Cyber-physical System Safety, Security, and Resilience Coengineering,” Software and Systems Modeling, 21(1), pp. 113–137, 2022.
Fleming, C., Elks, C., Bakirtzis, G., Adams, S., Carter, B., Beling, P., Horowitz, B., “Cyber-Physical Security Through Resiliency: A Systems-centric Approach,” Computer, 54(6), pp. 36–45, 2021.
Su, J., Adams, S., and Beling, P., “Value-Decomposition Multi-Agent Actor-Critics,” Proceedings of the AAAI Conference on Artificial Intelligence, 35, No. 13, pp. 11352–11360, 2021.
Lin, X., Adams, S., and Beling, P., “Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Games," Journal of Artificial Intelligence Research, 66, pp. 473–502, 2019.