Yuguang Fang

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About

Yuguang Fang is a leading researcher in wireless networking, mobile computing, and cyber-physical systems, with a focus on enabling intelligent, secure, and efficient communication for autonomous systems. His major contributions span collaborative perception for multi-robot and vehicular networks, device-free positioning using millimeter-wave signals, and privacy-aware management in autonomous devices. In his highly cited work on real-time adaptive collaborative perception (R-ACP), Fang introduced robust task-oriented communications to address extrinsic calibration errors in dynamic environments, significantly improving sensing accuracy and range—a breakthrough for autonomous navigation. His research on multi-target device-free positioning (DFP) leverages spatial-temporal mmWave point clouds to track objects without attached devices, advancing contactless sensing technologies. Additionally, his work on adversarial patches for robot vacuum cleaners pioneers privacy-aware defenses against inadvertent data breaches in smart homes. With over 17 citations on his recent 2025 paper alone, Fang’s innovations have profound implications for smart cities, autonomous driving, and IoT security. His achievements include pioneering frameworks for real-time, adaptive, and privacy-preserving systems, making him a pivotal figure in next-generation wireless and autonomous technologies.