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About

Liang Zhou is a leading researcher at the forefront of next-generation intelligent networks, specializing in digital twin (DT) empowered mobile systems, cross-modal communications, and human-robot collaboration. His work fundamentally addresses the challenges of enabling intelligent, privacy-preserving applications in resource-constrained and dynamic environments. Zhou’s major contributions include pioneering the concept of cloud-edge-client collaborative learning for digital twin mobile networks, a framework that leverages federated learning to break data silos while enhancing privacy. He has also made seminal advances in cross-modal signal reconstruction and semantic communications, particularly for remote healthcare and robotic teleoperation, where he tackles the critical issues of unstable links and limited computational resources by developing efficient, edge-based processing techniques. With over 80 citations across his most influential papers, his research on cross-view human intention recognition for human-robot collaboration is shaping the future of Industry 5.0. Notably, his 2023 work on general cross-modal signal reconstruction for teleoperation and his 2024 study on semantic communications for emergency networks demonstrate his commitment to solving real-world problems in healthcare, disaster response, and industrial automation. Zhou’s work is essential reading for anyone interested in the convergence of AI, edge computing, and multimodal interaction.