Keynote Speaker Ⅰ
Prof. Yang Yang
Chief Scientist of Terminus Technology Group, China
Dr. Yang Yang, IEEE Fellow, is a Professor at ShanghaiTech University and the Chief Scientist of IoT at Terminus Group, China. He is also an adjunct professor with the Department of Broadband Communication at Peng Cheng Laboratory and a Senior Consultant for Shenzhen Smart City Technology Development Group, China. Yang's research interests include multitier computing networks, 5G/6G systems, AIoT technologies, intelligent services and applications, and advanced wireless testbeds. He has published more than 300 papers and filed more than 120 technical patents in these research areas. He has been the Chair of the Steering Committee of AsiaPacific Conference on Communications (APCC) from 2019 to 2021. Currently, he is serving the IEEE Communications Society as the Chair for 5G Industry Community and Chair for Asia Region at Fog/Edge Industry Community.
Speech Title: Network AI Architecture for Everyone-Centric Customized Services
Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone centric customized services anywhere and anytime. In this talk, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system’s overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions.
Keynote Speaker Ⅱ
Prof. Meixia Tao
Shanghai Jiao Tong University, China
Meixia Tao is a Professor with the Department of Electronic Engineering, Shanghai Jiao Tong University, China. She received the B.S. degree in electronic engineering from Fudan University, Shanghai, China, in 1999, and the Ph.D. degree in electrical and electronic engineering from Hong Kong University of Science and Technology in 2003. Her current research interests include wireless edge learning, coded caching, reconfigurable intelligence surfaces, and physical layer multicasting. Dr. Tao is an IEEE Fellow, the recipient of the 2019 IEEE Marconi Prize Paper awards and the 2013 IEEE Heinrich Hertz Award for Best Communications Letters. She also receives the first prize of the 2020 Shanghai Natural Science Award.
Speech Title:Convergence of Computing and Communication in Mobile Networks
The functional positioning of 5G and future 6G mobile networks is undergoing a fundamental shift from connection-centric transmission pipelines to content-centric information services. To cope with this shift, the convergence of computing and communication has become an emerging trend for the artechitectural develoment of mobible networks. The idea is to push the conventional cloud-centric information services down to the edge of mobile networks by leveraging the ubiquitious computing and storage resources at both base stations and user devices. Typical computing-communication convergence techniques include edge caching, edge computing, and edge learning. This talk aims to provide an overview of these emerging techniques and discuss the recent research progress and potential applications.
Keynote Speaker Ⅲ
Prof. Defu Zhang
Xiamen University, China
Defu Zhang received the bachelor’s and master’s degrees in computational mathematics from Xiangtan University; the Ph.D. degree in computer software and theory from Huazhong University of Science and Technology; and the post doctorate degree from National University of Defense Technology. He was a Postdoctoral Researcher with the Financial Data Mining Group, Longtop.
He was selected as a leading talent of the “Double-Hundred Program” in Xiamen. He serves as a scholar of science communication in Minjiang; senior consultant of Xiamen Science, Technology and Economy Promotion Association; Top 100 Chinese Academic Innovators in Big Data; and expert of Public Welfare of China. He is also an influential leader in promoting China’s agricultural products in 2019. He has visited Hong Kong City University, University of Wisconsin Madison, and University of Macau.
Prof. Zhang has authored over 60 papers in SCI-indexed journals, each of which has been cited 32 times. He was involved in more than 10 projects supported by Huawei and the National Natural Science Foundation of China. He also founded www.pzcnet.com and we.retrees.cn that benefit the farmers and rural youngsters. He has been invited to many institutions at home and abroad to give academic reports and public lectures.
Keynote Speaker Ⅳ
Assoc. Prof. Shuangming Yang
Tianjin University, China
•Tianjin University (Tianjin, China) 09/2016-01/2020
PhD in Engineering
School of electrical and information engineering
•Tianjin University (Tianjin, China) 09/2013-06/2016
Master Degree in Engineering
School of electrical and information engineering
•Hebei University of Technology (Tianjin, China) 09/2009-06/2013
Bachelor Degree in Engineering Score Rank: 1/31
Scholarships for four consecutive years
Speech Title: Neuromorphic Computing for brain-inspired intelligence
In recent years, with the rapid development of artificial intelligence, it has become an important research hotspot in both academic and Internet fields. Although the emergence of deep learning and big data in recent years makes artificial intelligence surpass human beings in some tasks, it is powerless to deal with complex problems that human brain can deal with. At the same time, it needs a lot of computing resources and data resources as support. Brain like computing is a new brain inspired computing model, which is expected to break through the constraints of traditional computing framework and realize high-performance and strong cognitive computing.
Keynote Speaker Ⅴ
Dr. Arun Balodi
IEEE Senior Member，Visvesvaraya Technological University, Karnataka/Electronics and Communication Engineering
Dr Arun Balodi, Senior Member IEEE, Fellow IETE, Life Member ISTE is currently working as Professor & Head at Atria Institute of Technology in Bangalore, India. Dr Arun bestowed with PhD, Electrical Engineering, Indian Institute of Technology, Roorkee, India, 2018, M. Tech., Digital Signal Processing, Govind Ballabh Pant Engineering College, Pauri, India, 2010, and B.Tech. in Electronics and Communication Engineering, Uttar Pradesh Technical University, Lucknow, India, 2005. He was awarded Gold Medal in M. Tech., and the Academic Excellence award in the year 2010 and 2011. His research area in Biomedical Signal and Image Processing, Artificial Intelligence. Dr Arun has more than 14 years of teaching and research experience in the various institutions in India, which include Shobhit Institute of Engineering and Technology, Gangoh, Saharanpur, Shivalik College of Engineering, Dehradun, National Institute of Technology, Delhi, and presently, Atria Institute of Technology, Bangalore, India. Dr Arun also received MHRD scholarships during the research work at IIT Roorkee. His research area is Digital Signal and Image Processing, Medical Image Analysis, Machine Learning and Pattern Recognition. He has published more than 14 papers in peer-reviewed journals and international conferences. He is an active researcher and reviewer for various indexed Journals and Conferences and given various technical talks. He has actively participated in more than 22 FDPs and more than 100 webinars and conducted workshops in the domain of Signal and Image Processing. He is currently advising IEEE Student Branch at Atria Institute of Technology, Bangalore, for the IEEE Bangalore Section.
Keynote Speaker Ⅵ
Prof. Zhihui Lu
School of Computer Science, Fudan University
Prof. Zhihui Lu is a professor and doctoral supervisor at the School of Computer Science and Technology, Fudan University. 2004.7 Worked in Fudan University after graduating with a doctorate in computer science. 2009.10-2010.10 Visiting Scholar in the Department of Computer Science, Yale University.
Currently, he is the head of the Distributed System Monitoring and Management Research Office of the Engineering Research Center of the Ministry of Education for Network Information Security Audit and Monitoring, a key member of the Shanghai Blockchain Engineering Technology Research Center, an expert member of the Shanghai Content Distribution Network Engineering Technology Research Center, and China Information Expert member of the cloud computing standard working group of the Technical Standardization Committee, representative of the DMTF Distributed System Management Technology International Standards Organization University member, IEEE member, Vice Chairman of the International Service Computing Society Young Scientist Forum 2015-2016 China, Senior Member of the China Computer Federation, CCF Service Computing Member of the special committee, member of China Communications Society, member of Shanghai Computer Society
Speech Title: Key Technologies and Case Analysis of Cloud-edge computing Collaboration
As an extension of traditional cloud computing, the hybrid architecture of edge and cloud computing provides important support for Big data and AI task processing, and can process the highly dispersed massive data generated by terminal devices at the edge. There have been many successful cases of clou dedge collaboration technology, such as smart cities, Industrial Internet. However, there are still some problems to be solved. For example, the mismatch between resource-intensive intelligent model and resource-limited devices, the low efficiency of model collaborative scheduling strategy in a heterogeneous environment, and the lack of security and reliability of edge nodes and end devices. This report presents the development trend of cloud-edge computing collaboration technology, and gives some of our classic paper results and research and development cases, at last looks forward to the future development of cloud-edge computing collaboration technology.
Keynote Speaker Ⅶ
Dr. Ziyu Shao
IEEE Senior Member，ShanghaiTech University
Dr. Ziyu Shao is a tenured associate professor and the director of Network Intelligence Center in the School of Information Science and Technology, ShanghaiTech University.
Dr. Ziyu Shao received the B.S. and M.E. degrees from Peking University and the Ph.D. degree from The Chinese University of Hong Kong. He was a senior visiting researcher at EE Department of Princeton University in 2012, a visiting professor at EECS Department of UC Berkeley in 2017, and also a visiting scientist at the Simons Institute for the Theory of Computing at Berkeley. He was on the final list of top young researcher in Scientific Chinese 2015. He also obtained Google Faculty Teaching award in 2019.
Dr. Ziyu Shao is a senior member of IEEE, a member of board of governors of Shanghai Computer Society. His current research interests are networked AI system. He has published more than 70 papers in top journals and top conferences of IEEE and ACM.
Speech Title: Artificial Intelligence & Networks: An Unconsummated Union
During recent years, AI for networks and networks for AI have become research trends. In this talk, I will first introduce the history perspective on AI and networks. Then we present two case studies to show how AI and networks benefit each other. We conclude the talk with a discussion on Shannon-Style Thinking and its impact for integration of AI & networks in post-shannon era.