- 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.
According to my publications my topics of study are the ones on the right.
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.