Sebastian Kerz

Sebastian Kerz is a Researcher at the Chair of Automatic Control Engineering, Technical University of Munich (TUM), with a focus on learning-based and data-driven control systems. He has been affiliated with TUM since 2019, previously working at the University of Toronto and Technische Universität Darmstadt.

• Education:
  • Master of Science in Electrical Engineering and Information Technology (2015–2019) at TU Darmstadt, specializing in Automation Systems.
  • Bachelor of Science in Electrical Engineering and Information Technology (2011–2015) at TU Darmstadt.
  • Graduate Student at the University of Toronto's Department of Electrical & Computer Engineering (2017–2018).

His research centers on Data-driven Stochastic Model Predictive Control (MPC), integrating machine learning with control theory for systems in uncertain environments. Applications span power grids, building climate systems, and quadrocopters. He also explores robotic grasping in industrial recycling and Koopman operator learning for stable nonlinear systems.

Recent publications highlight advancements in adaptive stochastic MPC, uncertainty sampling, and safe control under data imperfections. His work bridges theoretical control frameworks with practical implementations, emphasizing robustness and efficiency.

At TUM, Kerz contributes to student supervision and project guidance, particularly in control and learning domains. He is available for thesis supervision and collaborative research endeavors.