Formal Methods for Safe and Interpretable Control
Modern engineering applications such as autonomous robots involve large-scale dynamical systems whose models are complex, uncertain, or only partially known, placing them beyond the reach of traditional model-based control techniques. Data-driven approaches, including machine learning and reinforcement learning, offer powerful tools for learning and decision-making in these settings, but often lack interpretability and provide limited guarantees of correctness and safety, which are critical requirements. Formal methods address these shortcomings by providing rich languages for expressing complex objectives and safety requirements, algorithmic techniques for verification and synthesis, and proofs that the resulting systems behave as intended. This talk brings together control theory, formal methods, and machine learning to create control systems that scale and adapt to uncertain or unknown dynamics while remaining safe, correct, and interpretable. Examples from autonomous driving and robotic manipulation illustrate how this integration enables the synthesis of controllers for complex, safety-critical tasks.
Calin Belta
Brendan Iribe Endowed Professor of Electrical and Computer Engineering and Computer Science, University of Maryland, College Park on September 25, 2026 at 10:15 AM in EB2 1231
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Calin Belta is the Brendan Iribe Endowed Professor of Electrical and Computer Engineering and Computer Science at the University of Maryland, College Park, where he is also part of the Maryland Robotics Center and the Institute for Systems Research. His research focuses on making control and machine learning systems safe and interpretable, with particular emphasis on robotics and systems biology. Notable awards include the AFOSR Young Investigator Program award, the NSF CAREER award and the HSCC Test of Time award. He is a Fellow of IEEE.
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