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Jiannong Cao

Professor, IEEE Fellow

Hong Kong Polytechnic University

Title: Collaborative Task Execution in Advanced Edge Computing Environment


Advances in edge computing will boost more smart applications including AI models, AR, video analytics, and Industrial IoT applications. In an advanced edge computing environment, edge nodes of multiple stockholders are interconnected to facilitate the share of data and computation resources, and to collaborate on joint task execution using the shared resources. A fundamental issue is how to optimize the performance of collaborative task execution in terms of various metrics. In this talk, I will first introduce the major approaches to collaborative task execution, i.e., task partitioning, task allocation and task migration. In particular, I will present our recent work on multi-user multi-resource task partitioning, which is a new challenge and was not solved before. I will also highlight our works in data aware task allocation and task migration addressing the challenges arising from collaborative edge computing. Finally, I will conclude the talk by pointing out some future directions in this topic area.


Dr. Cao is currently a Chair Professor of Department of Computing at The Hong Kong Polytechnic University, Hong Kong. He is also the director of the Internet and Mobile Computing Lab in the department and the director of University’s Research Facility in Big Data Analytics. He served the department head from 2011 to 2017.
Dr. Cao’s research interests include parallel and distributed computing, wireless sensing and networks, pervasive and mobile computing, and big data and cloud computing. He has directed and participated in over 90 research and development projects and, as a principal investigator, obtained over HK$60 million grants from various funding agencies. He published 5 co-authored and 9 co-edited books, and over 500 papers in major international journals and conference proceedings. Dr. Cao also received Best Paper Awards from journals and conferences including IEEE TII, DSAA’2017, SMARTCOMP 2016, ISPA 2013, and IEEE WCNC 2011.
Dr. Cao served the Chair of the Technical Committee on Distributed Computing of IEEE Computer Society 2012-2014, and a member of many IEEE and other professional committees. He has served as chairs and members of organizing and technical committees of many international conferences, and as editors of many international journals. Dr. Cao is a fellow of IEEE and ACM distinguished member. In 2017, he received the Overseas Outstanding Contribution Award from China Computer Federation.


Rui Zhang

Associate Professor, IEEE Fellow

Department of Electrical and Computer Engineering, National University of Singapore

Title: Accessing From the Sky: UAV Communications for 5G and Beyond


The integration of the dramatically increasing volume of unmanned aerial vehicles (UAVs) or drones into the future 5G and beyond wireless networks calls for a paradigm shift on the design of traditional cellular systems for terrestrial-only communications. In particular, how to realize cellular-connected UAV communications and UAV-assisted terrestrial communications are both practically appealing as well as challenging, due to the high altitude and high mobility of UAVs, the unique channel characteristics of UAV-ground links, the stringent constraints imposed by the size, weight and power (SWAP) limitations of UAVs, as well as the new degree of freedom by exploiting UAV placement/trajectory and communication co-design. Substantial research efforts from both academia and industry have been devoted to advancing this exciting new field, with remarkable progress made in the past couple of years. In this talk, we will provide a comprehensive overview of the state-of-the-art results on UAV-cellular integration, with a particular emphasis placed on the latest research findings on aerial-ground interference management and trajectory optimization in UAV communications. Open challenges and promising directions for future research will be highlighted.


Dr. Rui Zhang (IEEE Fellow) received the Ph.D. degree from Stanford University in electrical engineering in 2007. He is now a Dean’s Chair Associate Professor in the Department of Electrical and Computer Engineering, National University of Singapore. His current research interests include wireless information and power transfer, UAV communications, and reconfigurable MIMO. He has published over 350 papers, which have been cited more than 30,000 times. He has been listed as a Highly Cited Researcher by Thomson Reuters/Clarivate Analytics since 2015, and was recognized in both the scientific fields of Computer Science and Engineering in 2018. His works have received several IEEE awards, including the IEEE Marconi Prize Paper Award in Wireless Communications, the IEEE Communications Society Heinrich Hertz Prize Paper Award, the IEEE Signal Processing Society Best Paper Award, Young Author Best Paper Award and Donald G. Fink Overview Paper Award. He has served as an Editor for several IEEE journals, including TWC, TCOM, JSAC, TSP, TGCN, etc., and as TPC co-chair or organizing committee member for over 30 international conferences.  He is an IEEE Distinguished Lecturer of IEEE Communications Society and IEEE Signal Processing Society.


Chee Wei Tan

Associate Professor

City University of Hong Kong

Title: Network Centrality as Statistical Inference in Large Networks


Massive data-sets are often generated by a large network of users. And large networks represent a fundamental medium for the spreading and diffusion of various information where the actions of certain users increase the susceptibility of other users to the same; this results in the successive spread of information from a small set of initial users to a much larger set. Examples include the spread of rumors in online social networks and power outage in smart grids. Modeling these massive data-sets as huge graphs, we introduce the idea of network centrality as statistical inference in large networks to solve two optimization problems, namely rooting out rumor sources and averting cascading failures. A network centrality with statistical basis accurately captures the optimality of the problems, and brings graph algorithm machinery to bear on solving the problems. We conclude the talk with insights on putting the theory into practice in graph analytics software development.


Dr. Tan is an Associate Professor of computer science with the City University of Hong Kong. He was a Postdoctoral Scholar at the California Institute of Technology and a Senior Fellow with the Institute for Pure and Applied Mathematics for the program on “Science at Extreme Scales”. His research interests include networks and graph analytics, artificial intelligence, algorithms at the interface of computer science and statistics, and convex optimization theory and its applications. He currently serves as an Editor for the IEEE/ACM Transactions on Networking.





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