- Koopman-Equivariant Gaussian Processes. TUM, Lehrstuhl für Informationstechnische Regelung (ITR), 2024, more…
- Koopman Kernel Regression. Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (NeurIPS Proceedings), 2023 more…
- Diffeomorphically Learning Stable Koopman Operators. IEEE Control Systems Letters (L-CSS) 6, 2022, 3427 - 3432 more…
Max Beier
- Tel.: +49 (89) 289 - 25779
- Raum: 0305.04.522
- max.beier@tum.de
Short Biography
| Seit 05/2024 | Associate PhD |
| Since 11/2023 | Research Assistent Chair of Information-oriented Control Technical University of Munich |
| 10/2020-07/2023 | Master of Science - Robotics, Cognition, Intelligence Technical University of Munich Focus: Machine Learning for Dynamical Systems and Control |
| 10/2017-09/2020 | Bachelor of Engineering - Mechanical Engineering Baden-Wuerttemberg Cooperative State University (DHBW) Stuttgart Robert Bosch GmbH |
Research Interests
- Principled Machine Learning
- Dynamical Systems and Operator Theory
- Learning-based Control
My research revolves around finding learning-based dynamical system representations. I am especially interested in geometric and operator theoric methods. The overarching goal is to build models from data that allow for convenient automated control design.
Currently Listed Topics
Data-driven Semigroup Equivariant Neural Models (MA)
Representing Controlled Transfer Operators for Convex Optimal Control (FP/MA)
Contact
I am always looking forward to working with motivated students. If you are curious about my research and looking for a thesis, do not hesitate to contact me. If there is no topic on display, please specify which topics you are interested in. Please include your CV, a current transcript of records, and your preferred start date.