Papers
16
Total Citations
251
H-Index
8
About
Shayegan Omidshafiei is a robotics and artificial intelligence researcher whose work sits at the intersection of multi-robot coordination, decision-making under uncertainty, and machine learning. His most influential contributions center on Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs), a powerful framework for modeling cooperative multi-robot planning in complex, partially observable environments. His seminal papers on belief space macro-actions for decentralized control have collectively garnered over 120 citations, establishing him as a key voice in scalable multi-agent planning in continuous state spaces. Omidshafiei has also advanced data-driven approaches to multi-robot cooperation, developing algorithms that enable agents to learn coordination strategies without requiring complete environmental models — a critical capability for real-world deployment. His work spans both theoretical foundations and practical applications, including forest fire management, heterogeneous multiagent learning under limited communication, and safety certification of neural network-controlled systems through backward reachability analysis. His Measurable Augmented Reality platform (MAR-CPS) further demonstrates his commitment to bridging algorithmic research with physical hardware prototyping. Across his career, Omidshafiei has consistently tackled some of the hardest challenges in autonomous systems: uncertainty, scalability, and safe real-world operation.
Research Focus
Key Achievements
Top Papers
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- 7MAR-CPS: Measurable Augmented Reality for Prototyping Cyber-Physical Systems12 citations · 2015
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