Papers
11
Total Citations
205
H-Index
7
About
Dominik Baumann is a researcher whose work sits at the intersection of wireless networked control systems, cyber-physical systems, and machine learning for dynamical systems. He has made significant contributions to the challenge of closing feedback control loops over low-power wireless networks — a problem critical to emerging applications in smart manufacturing, swarm robotics, and autonomous vehicles. His most cited work, "Wireless Control for Smart Manufacturing" (2020, 69 citations), examines how wireless communication can enable more flexible and scalable industrial processes, while his "Feedback Control Goes Wireless" series (2019, 47 citations) rigorously addresses stability guarantees in multi-hop wireless environments operating under tight latency constraints. Baumann has also explored scalability challenges in networked control, proposing solutions that maintain stability even under bandwidth overload conditions. Beyond networking, his research spans neuromorphic computing applied to humanoid robot control, causal structure identification in dynamical systems, and safe Bayesian optimization through his GoSafeOpt framework, which enables safe global exploration during learning on physical systems. With over 200 cumulative citations, Baumann's body of work bridges rigorous control theory with practical wireless and data-driven methods, making him a notable contributor to the future of intelligent, connected autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2Feedback control goes wireless47 citations · 2019
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- 9Identifying Causal Structure in Dynamical Systems2 citations · 2020
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