Michael A. Bender

Harvard University Press, Stony Brook University

Papers

13

Total Citations

531

H-Index

10

About

Michael A. Bender is a prominent computer scientist whose research sits at the intersection of algorithmic theory, robotics, and distributed computing. He is best known for his foundational contributions to swarm robotics, particularly in the areas of graph exploration, robot dispersion, and multi-agent coordination in unknown environments. Bender's most influential work addresses how robots — often modeled as simple, resource-limited agents — can collectively and efficiently explore and map complex environments. His landmark paper on team graph exploration (181 citations) demonstrated that just two cooperating robots can learn any strongly-connected directed graph in polynomial expected time, a striking result that elegantly illustrates the power of collaboration in autonomous systems. His pioneering work on the "Freeze-Tag Problem" introduced an entirely new class of optimization challenges in swarm robotics: how to optimally awaken a dormant robot swarm from a single active agent, spawning multiple follow-up studies on approximation algorithms and heuristic analysis. With several papers exceeding 100 citations and a sustained research program spanning multiple years, Bender has meaningfully shaped theoretical robotics and algorithm design. His work provides essential mathematical foundations for researchers building real-world multi-robot systems, making him a key reference point for anyone entering the field of autonomous swarm coordination.

Research Focus

Key Achievements

10
H-Index
13
Papers
531
Total Citations
41
Avg Citations/Paper
🏆 Most Cited Paper
The power of team exploration: two robots can learn unlabeled directed graphs
181 citations · 2002
📈 Most Prolific Year: 2002 (5 Papers)
🤝 Key Collaborators: 13
🏛 Institutions: Harvard University Press, Stony Brook University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 15 days ago