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About

Shouxiang Ni is a leading researcher at the forefront of intelligent mobile networks and human-robot collaboration, with a focus on integrating digital twin (DT) technology, edge computing, and multimodal communication. His work addresses critical challenges in privacy-preserving AI and remote healthcare, particularly through cloud-edge-client collaborative learning frameworks that enhance federated learning in DT-empowered mobile networks. Ni’s pioneering research on cross-modal communications—combining audio, video, and haptic signals—has advanced human intention recognition, enabling more precise and efficient human-robot interaction in industrial and healthcare settings. His most-cited papers, including “Cloud-Edge-Client Collaborative Learning in Digital Twin Empowered Mobile Networks” and “Edge-Based Cross-Modal Communications for Remote Healthcare,” each with 27 citations, highlight his impact on 6G-enabled applications. Ni’s work on cross-view human intention recognition and tactile feedback systems further underscores his contributions to natural, multi-sensory human-computer interaction. With a growing citation record and innovative solutions for real-world challenges like infectious disease mitigation, Ni is shaping the future of intelligent, collaborative systems.