Prof. Dr.-Ing. Thomas Beckers
Biography
Thomas Beckers is Professor of Autonomous Systems and Head of the Institute of Measurement, Control, and Microtechnology at Ulm University, Germany. Until August 2026, he was an Assistant Professor of Computer Science at Vanderbilt University. Prior to that, he was a postdoctoral researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania, where he was a member of the GRASP Laboratory and the PRECISE Center. In 2020, he earned his doctorate in Electrical Engineering at the Technical University of Munich (TUM), Germany. He received the B.Sc. and M.Sc. degree in Electrical Engineering in 2010 and 2013, respectively, from the Technical University of Braunschweig, Germany. In 2018, he was a visiting researcher at the University of California, Berkeley. He is a DAAD AInet fellow and was awarded with the Rhode & Schwarz Outstanding Dissertation price. His research interests include physics-enhanced learning, Bayesian models, and safe learning-based control.
Research
Modern applications such as autonomous driving, soft robotics, and human–robot interaction push today’s technical systems to unprecedented levels of complexity. As a result, traditional modeling and control approaches are often prohibitively time-consuming or even infeasible. Data-driven methods offer a powerful alternative: they require little expert tuning and have demonstrated impressive performance. Yet their lack of guarantees has so far limited their use in safety-critical systems.
Our research addresses this gap by advancing safe, learning-based modeling and control of physical systems. We develop novel data-driven control methods that combine high performance with rigorous safety guarantees in closed-loop operation. A key focus is on integrating physical prior knowledge into learning algorithms, improving their generalizability, reliability, and interpretability. Ultimately, our work contributes to the next generation of safe, robust, and intelligent control technologies for autonomous systems.
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Publikationen
Die Publikationen finden Sie in der Publikationsdatenbank des Instituts.
