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About

Hao Xing is a researcher working at the intersection of medical robotics, computer vision, and human-robot interaction, with a focus on developing intelligent systems that enhance precision, safety, and accessibility in both clinical and collaborative environments. His most cited work addresses 6DOF needle pose estimation for robot-assisted vitreoretinal surgery, tackling the formidable challenges of illumination variation and high-precision navigation in one of medicine's most delicate procedures, earning 27 citations since 2019. Xing has also made significant contributions to telemedicine, proposing a dual doctor-patient digital twin framework for remote examination, diagnosis, and rehabilitation, reflecting a timely response to global healthcare pressures and accumulating 24 citations. Beyond medical applications, his research extends into action recognition and human activity understanding, where he employs graph convolutional networks and spatiotemporal modeling to enable robots to interpret complex human behaviors in collaborative settings. His progression from pyramid graph networks to class-level attention mechanisms and uncertainty-aware activity recognition demonstrates a consistent drive toward more robust, context-aware AI systems. Collectively, Xing's work bridges surgical robotics, remote healthcare, and intelligent human-robot collaboration, positioning him as an emerging voice in applied AI for real-world human-centered systems.