Mingkai Tang
Papers
1
Total Citations
1
H-Index
1
About
Mingkai Tang is a leading researcher in embodied intelligence and multi-agent systems, with a focus on scalable coordination for autonomous robotics and smart transportation. His most impactful work centers on GPU-accelerated algorithms for multi-agent pathfinding (MAPF), a critical challenge in enabling efficient, collision-free navigation for large teams of robots or vehicles in dynamic environments. Tang’s 2025 paper on GPU-accelerated Conflict-based Search introduces a novel approach that leverages parallel computing to dramatically speed up MAPF solutions, paving the way for real-time deployment in complex, real-world settings. This work has already garnered early citations, signaling its significance to the field. Beyond this, Tang’s research integrates insights from artificial intelligence, high-performance computing, and robotics, addressing the scalability bottlenecks that have long limited multi-agent coordination. His contributions are particularly vital for applications like warehouse automation, autonomous fleets, and smart city traffic management. By bridging theoretical advances with practical, hardware-accelerated methods, Tang is helping to unlock the next generation of embodied intelligence, where multiple agents can operate seamlessly and safely alongside humans.
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Top Papers
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