Prof. Feifei Gao, IEEE Fellow

Department of Automation, Tsinghua University, China

Biography: Feifei Gao (Fellow, IEEE) received the B.Eng. degree from Xi'an Jiaotong University, Xi'an, China in 2002, the M.Sc. degree from McMaster University, Hamilton, ON, Canada in 2004, and the Ph.D. degree from National University of Singapore, Singapore in 2007. Since 2011, he joined the Department of Automation, Tsinghua University, Beijing, China, where he is currently a tenured full professor.  

Prof. Gao's research interests include signal processing for communications, array signal processing, convex optimizations, and artificial intelligence assisted communications. He has authored/coauthored more than 200 refereed IEEE journal papers and more than 150 IEEE conference proceeding papers that are cited more than 18000 times in Google Scholar. Prof. Gao has served as an Editor of IEEE Transactions on Wireless Communications, IEEE Journal of Selected Topics in Signal Processing (Lead Guest Editor), IEEE Transactions on Cognitive Communications and Networking, IEEE Signal Processing Letters (Senior Editor), IEEE Communications Letters (Senior Editor), IEEE Wireless Communications Letters, and China Communications. He has also served as the symposium co-chair for 2019 IEEE Conference on Communications (ICC), 2018 IEEE Vehicular Technology Conference Spring (VTC), 2015 IEEE Conference on Communications (ICC), 2014 IEEE Global Communications Conference (GLOBECOM), 2014 IEEE Vehicular Technology Conference Fall (VTC), as well as Technical Committee Members for more than 50 IEEE conferences.

Speech Title: Key Technologies and Prototype Design for Integrated Sensing and Communications System

Abstract: In the future, millions of base stations (BSs) and billions of users (UEs) will natively build an integrated sensing and communications (ISAC) system, which can utilize intelligent ubiquitous methods to realize the ultimate goal of sensing, i.e., constructing the global mapping from real physical world to digital twin world, while providing communications services at the same time. For this purpose, we conduct a series of theoretical and technical researches on ISAC, in which we decompose the real physical world into static environment, dynamic targets, and various object materials. The ubiquitous static environment occupies the vast majority of the physical world, for which we design static environment reconstruction (SER) scheme to obtain the layout and point cloud information of static buildings. The dynamic targets floating in static environments create the spatiotemporal transition of the physical world, for which we design comprehensive dynamic target sensing (DTS) scheme to detect, estimate, track, image and recognize the dynamic targets in real-time. The object materials enrich the electromagnetic laws of the physical world, for which we develop object material recognition (OMR) scheme to estimate the electromagnetic coefficient of the objects. Finally, based on these theoretical researches, we build an ISAC hardware prototype platform working in millimeter wave frequency band, realizing high-precision SER, DTS, and basic OMR, which provides preliminary verification for building the digital twin for communications networks.

Prof Kang Li, Chair of Smart Energy Systems

School of Electronic and Electrical Engineering, University of Leeds, UK

Biography: Prof Kang Li holds the Chair of Smart Energy Systems and the Director of the Institute of Communication and Power Networks at School of Electronic and Electrical Engineering, University of Leeds. His recent research interest lies in the development of machine learning and control technologies to support transport decarbonization, funded by over 50 research projects, totaling £20M. He has published over 200 papers in international journals, winning 20 prizes and awards, and has been invited to give over 100 keynotes and invited talks worldwide. 

Speech Title: Machine Learning in Green Transportation – Some Case Studeis

Abstract: The transport decarbonization increases the coupling of the transport and energy systems, demanding a holistic and data-driven approach to manage and operate the systems with increased complexity. This talk presents several case studies in the application of machine learning techniques in holograph based state estimation and management of electric vehicles in complex urban traffic environments.