Shiwei Fang

University of North Carolina at Chapel Hill, University of North Carolina Health Care, Augusta University Health

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About

Shiwei Fang is an emerging researcher specializing in wireless sensing, RF-based perception, and human-environment interaction technologies. His work sits at the intersection of millimeter-wave radar systems, WiFi-based sensing, and artificial intelligence, with a particular focus on enabling robust situational awareness in scenarios where conventional optical sensors fall short. Fang's most notable contribution is the development of SuperRF, a system that leverages low-cost, off-the-shelf 77GHz mmWave radar hardware to generate enhanced 3D RF scene representations. This work, which has garnered 8 citations, addresses critical privacy and environmental limitations of camera-based systems, making it especially valuable for indoor and sensitive deployment contexts. Complementing this, his earlier AI-Enhanced 3D RF Representation work laid foundational groundwork for applying machine learning to radar signal processing. His research on Non-Line-of-Sight human presence detection using commodity WiFi devices demonstrates a strong interest in enabling safer human-robot coexistence, helping robots perceive people obscured by physical obstacles. More recently, his involvement in the IoBT-MAX multimodal testbed highlights a growing focus on battlefield-oriented edge computing research. Collectively, Fang's publications reflect a trajectory toward practical, low-cost sensing solutions with real-world impact across robotics, security, and defense applications.