GRK 3081:  Machine Learning and Control Theory: Exploring Synergies, Complementarities, and Mutual Benefits (METEOR)

• Subject Area: Computer Science ; Electrical Engineering and Information Technology
• Term: since 2026
• Website: Homepage
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 534429653

In light of the current challenges in the field of artificial intelligence, the METEOR Research Training Group is dedicated to unifying machine learning (ML) and control theory (CT) and strengthening the interaction between the two disciplines. Despite common interests and methods, the two fields have developed different languages and cultures, largely independently of each other. The combination of the learning-centered, data-driven approach of ML and the primarily model-based perspective of CT appears highly promising, but requires scientists with expertise on both sides. By combining cutting-edge research and comprehensive interdisciplinary training, METEOR will produce such a new generation of researchers at the intersection of ML and CT. The research program focuses on two main directions: first, how ML can support the data-driven design of robust control for complex, safety-critical applications ("ML for CT"), and second, how concepts and methods of CT can contribute to the improvement of ML algorithms ("CT for ML"). Both directions are approached from the perspective of complex dynamical systems that form a common mathematical framework. The research addresses four main topics: (1) modeling and quantifying uncertainty for robust control, (2) representations for dynamical systems and control, (3) control theory for designing machine learning algorithms, and (4) formal analysis of machine learning algorithms using control theory. METEOR's qualification program places a strong emphasis on interdisciplinarity. Currently, engineering students often have a solid background in calculus, differential equations and scientific computing, while computer scientists and statisticians are usually well versed in optimization, data analysis and ML. However, the interdisciplinary understanding between ML and CT is usually limited. A newly designed lecture "Machine Learning and Control Theory" as well as research-oriented seminars and a reading and writing club are therefore intended to create a common language and basic understanding between the two disciplines. Innovative measures such as interdisciplinary workshops and annual hackathons build a further bridge between ML and CT and provide practical experience. In order to prepare graduates for a successful career in science and industry, the program is rounded off with targeted soft skills courses and international research stays.

• DFG Programme: Research Training Groups
• Applicant Institution: Ludwig-Maximilians-Universität München
• Co-Applicant Institution: Technische Universität München (TUM)
• Spokesperson: Professor Dr. Eyke Hüllermeier
• Participating Researchers: Professor Dr.-Ing. Matthias Althoff; Professor Dr. Massimo Fornasier; Professorin Dr.-Ing. Sandra Hirche; Professorin Dr. Gitta Kutyniok; Professor Christian Kühn, Ph.D.; Professor Dr. Johannes Maly; Professor Dr. David Rügamer; Professorin Dr. Angela Schoellig; Professor Dr. Volker Tresp