Dominik Baumann
Assistant Professor
Assistant Professor
T410 Dept. Electrical Engineering and Automation
Dominik Baumann is an Assistant Professor at Aalto University since 2023. His research interests revolve around systems and control theory, machine learning, and communication networks.
Full researcher profile
https://research.aalto.fi/...
E-post
dominik.baumann@aalto.fi
Telefonnummer
+358504773899
Kompetensområde
213 Electronic, automation and communications engineering, electronics, Automation and systems technology
Utmärkelser
Future Award
2019 Future Award of the Ewald Marquardt Foundation
Award or honor granted for a specific work
Department of Electrical Engineering and Automation
Jan 2019
Best Demo Award
Best Demo Award at the 2019 ACM/IEEE International Conference on Information Processing in Sensor Networks
Award or honor granted for a specific work
Department of Electrical Engineering and Automation
Jan 2019
Best Paper Award
Best Paper Award at the 2019 ACM/IEEE International Conference on Cyber-Physical Systems
Award or honor granted for a specific work
Department of Electrical Engineering and Automation
Jan 2019
Forskningsgrupp
- Cyber-physical Systems
Publikationer
Ergodicity in reinforcement learning
Dominik Baumann, Erfaun Noorani, Arsenii Mustafin, Xinyi Sheng, Bert Verbruggen, Arne Vanhoyweghen, Vincent Ginis, Thomas B. Schön
2026
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Integrating Lagrangian Neural Networks into the Dyna Framework for Reinforcement Learning
Shreya Das, Kundan Kumar, Muhammad Iqbal, Outi Savolainen, Dominik Baumann, Laura Ruotsalainen, Simo Särkkä
2026
Proceedings of the European Signal Processing Conference
Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
Qingyun Guo, Junyi Shi, Tomasz Kucner, Dominik Baumann
2026
IFAC Proceedings Volumes
Computationally lightweight classifiers with frequentist bounds on predictions
Shreeram Murali, Cristian R. Rojas, Dominik Baumann
2026
Proceedings of Machine Learning Research
Online Bayesian Experimental Design for Partially Observed Dynamical Systems
Sara Pérez-Vieites, Sahel Iqbal, Simo Särkkä, Dominik Baumann
2026
Proceedings of Machine Learning
Beyond expected value: geometric mean optimization for long-term policy performance in reinforcement learning
Xinyi Sheng, Dominik Baumann
2026
2025 IEEE 64th Conference on Decision and Control, CDC 2025
Safe Bayesian optimization across noise models via scenario programming
Abdullah Tokmak, Thomas B. Schon, Dominik Baumann
2026
2026 American Control Conference, ACC 2026
Towards safe control parameter tuning in distributed multi-agent systems
Abdullah Tokmak, Thomas B. Schön, Dominik Baumann
2026
2025 IEEE 64th Conference on Decision and Control, CDC 2025
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations
Dominik Baumann, Erfaun Noorani, James Price, Ole Peters, Colm Connaughton, Thomas B. Schön
2025
Transactions on Machine Learning Research
Safety and optimality in learning-based control at low computational cost
Dominik Baumann, Krzysztof Kowalczyk, Cristian R. Rojas, Koen Tiels, Paweł Wachel
2025
IEEE Transactions on Automatic Control