Papers
2
Total Citations
3
H-Index
1
About
Chao Fang is a rising researcher at the forefront of embodied intelligence and efficient machine learning systems. His work focuses on two critical challenges for next-generation autonomous robotics: enabling on-device learning and coordinating multi-agent teams. Fang’s pioneering research on Microscaling (MX) processing introduces a novel precision-scalable hardware architecture that dramatically reduces energy consumption for edge training, allowing robots to adapt to new environments without cloud dependency. This breakthrough is essential for real-time, low-power robotics applications. Additionally, Fang has advanced multi-agent coordination by developing a GPU-accelerated Conflict-based Search algorithm, significantly speeding up pathfinding for swarms of autonomous agents in dynamic environments. His work on the multi-agent pathfinding (MAPF) problem directly addresses core challenges in smart transportation and collaborative robotics. With his most-cited paper already garnering attention in 2025, Chao Fang is establishing himself as a key innovator in hardware-software co-design for embodied AI, pushing the boundaries of what autonomous systems can achieve efficiently and collaboratively.
Research Focus
Key Achievements
Top Papers
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