AI will be tightly integrated with wireless networks in future 6G wireless systems. On one hand, more and more AI applications will be running directly on mobile terminals for inferencing and training because dataset exists locally at mobile or IoT devices but large neural network models will be in the cloud. As such, wireless communication networks will be “in-the-loop” between mobile devices and the neural network models. it is important for future wireless networks to be optimised to support more efficient AI deployment. On the other hand, AI as a powerful tool can have huge potential to solve challenging problems in wireless communications design, resulting in better and more efficient wireless networks. There are lots of potential to deploy domain-specific AI design to implement challenging modules such as the physical layer and system designs in wireless systems. In this talk, we shall explore the integration of AI and future 6G wireless networks.
Professor Vincent obtained B.Eng (Distinction 1st Hons) from the University of Hong Kong (1989-1992) and Ph.D. from the Cambridge University (1995-1997). He joined Bell Labs from 1997-2004 and the Department of ECE, Hong Kong University of Science and Technology (HKUST) in 2004. He is currently a Chair Professor and the Founding Director of Huawei-HKUST Joint Innovation Lab at HKUST. He is also elected as the fellow of the Hong Kong Academy of Engineering Sciences, IEEE Fellow, HKIE Fellow, Croucher Senior Research Fellow and Changjiang Chair Professor. Vincent has published more than 400 IEEE journal and conference papers and has contributed to 50 US patents on various wireless systems as well as 4 IEEE standard contributions. His current research focus includes Stochastic Optimization and Analysis for wireless systems, Massive MIMO Systems, Sparse Recovery, Bayesian Machine Learning, Mission-Critical IoT as well as PHY Caching for Wireless Networks.
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
