Mark Mazumder
Papers
5
Total Citations
170
H-Index
4
About
Mark Mazumder is a leading researcher at the intersection of multi-robot systems, cybersecurity, and resource-constrained machine learning. His work addresses the critical challenge of ensuring trust and resilience in autonomous networks, most notably through his foundational paper "Guaranteeing Spoof-Resilient Multi-Robot Networks" (2017, 90 citations), which provides formal guarantees against cyber-attacks that threaten coordination in aerial surveillance and delivery fleets. Mazumder further advanced multi-robot coordination with "Active Rendezvous for Multi-robot Pose Graph Optimization Using Sensing over Wi-Fi" (2022, 23 citations), enabling more efficient localization in communication-limited environments. Recognizing the growing importance of edge AI, he co-authored the influential "Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots" (2022, 38 citations), which maps the frontier of deploying ML on low-cost autonomous robots—a domain that stress-tests traditional ML system design. His work bridges theoretical security guarantees with practical, hardware-aware learning, making him a pivotal figure in shaping robust, intelligent, and deployable multi-robot systems for real-world applications.
Research Focus
Key Achievements
Top Papers
- 1Guaranteeing spoof-resilient multi-robot networks90 citations · 2017
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- 4Guaranteeing Spoof-Resilient Multi-Robot Networks15 citations · 2015
- 5