Biographie

Thomas Beckers is an Assistant Professor of Computer Science and the Institute for Software Integrated Systems at Vanderbilt University. Before joining Vanderbilt, he was a postdoctoral researcher at the Department of Electrical and Systems Engineering, University of Pennsylvania, where he was member of the GRASP Lab, PRECISE Center and ASSET 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 prize. His research interests include physics-enhanced learning, nonparametric models, and safe learning-based control. 

Research Focus

Closing the gap between machine learning and control theory to develop new algorithms towards safe, robust, and intelligent control of complex systems

Selected Publications

Thomas Beckers, Dana Kulić, and Sandra Hirche. “Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems”.
In: Automatica 103 (2019), pp. 390–397. doi: 10.1016/j.automatica.2019.01.023

Thomas Beckers, Leonardo J. Colombo, Sandra Hirche, and George J. Pappas. “Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics”.
In: IEEE Control Systems Letters (L-CSS). 2022. doi: 10.1109/LCSYS.2021.3138546.

Thomas Beckers and Sandra Hirche. “Prediction with Approximated Gaussian Process Dynamical Models”.
In: Transaction on Automatic Control. 2022. doi: 10.1109/TAC.2021.3131988.

Thomas Beckers, Jacob H. Seidman, Paris Perdikaris, George J. Pappas. “Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior”.
In: Proceedings of the Conference on Decision and Control. 2022. doi: 10.1109/CDC51059.2022.9992733.

Selected Media