Cen Chen

Agency for Science, Technology and Research, South China University of Technology, Hunan University

Papers

Total Citations

119

H-Index

About

Dr. Cen Chen is a leading researcher at the intersection of 3D computer vision, robotics, and efficient deep learning. His work primarily focuses on enabling intelligent systems to perceive and interact with the physical world, with key contributions in point cloud acceleration and few-shot learning. Dr. Chen’s most impactful work, “WiFi-Based Indoor Robot Positioning Using Deep Fuzzy Forests” (92 citations), addresses the long-standing challenge of mobile robot localization without relying on expensive visual sensors, pioneering a fusion of fuzzy logic and deep learning for robust indoor navigation. He has made significant strides in hardware-aware AI, developing novel accelerators like *SimDiff* that exploit geometric and spatial similarity in point clouds to dramatically reduce latency and energy consumption—critical for real-time applications in autonomous driving and robotics. His research on Cascade Graph Neural Networks further pushes the boundary of few-shot learning on 3D data, enabling models to generalize from minimal labeled examples. With a growing body of work that bridges algorithmic innovation and practical deployment, Dr. Chen is shaping the future of efficient, perception-driven autonomous systems.