Thomas M. Wheeler

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About

Thomas M. Wheeler is a researcher at the intersection of robotics, control theory, and artificial intelligence, with a primary focus on resilient multi-robot systems and sequential decision-making under uncertainty. His most influential work, “Switching Topology for Resilient Consensus using Wi-Fi Signals” (2019, 16 citations), addresses a critical vulnerability in multi-robot teams: malicious attacks on communication networks. Wheeler introduces a novel framework that exploits the physical nature of wireless signals to secure consensus protocols, enabling robot teams to maintain coordination even when some agents are compromised. This work is foundational for the safe deployment of autonomous systems in real-world environments. In parallel, Wheeler’s research on reinforcement learning for partially observable Markov decision processes (POMDPs), detailed in his 2020 paper, develops partitioned rollout and policy iteration algorithms for autonomous sequential repair problems. This contribution advances the practical application of reinforcement learning in domains where agents must act with incomplete information, such as infrastructure maintenance and disaster response. Wheeler’s work is notable for bridging theoretical rigor with pressing real-world challenges, establishing him as a rising voice in secure and resilient autonomy.