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Keynotes

 

Jiannong Cao

Professor, IEEE Fellow

Hong Kong Polytechnic University

Title: TBA

Abstract:

TBA

Biography:

Professor 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. His research interests include parallel and distributed computing, wireless sensing and networks, pervasive and mobile computing, and big data and cloud computing. He has co-authored 5 books, co-edited 9 books, and published over 600 papers in major international journals and conference proceedings. He received Best Paper Awards from conferences including, IEEE Trans. Industrial Informatics 2018, IEEE DSAA 2017, IEEE SMARTCOMP 2016, IEEE/IFIP EUC 2016, IEEE ISPA 2013, IEEE WCNC 2011, etc.

Sumei Sun

Professor, IEEE Fellow

Institute for Infocomm Research (I2R), Singapore

Title: TBA

Abstract:

TBA

Biography:

Dr. Sumei SUN has been with the Institute for Infocomm Research since 1995. She was the Communication Systems and Signal Processing (CSSP) Technology Group Leader during 2000 to 2002, Modem Technology Laboratory Head during 2003 to 2006, and Head of Modulation & Coding Department since 2007. Her recent research interests include 5G transmission technologies, energy– and spectrum–efficient wireless communication systems, renewable energy management and cooperation in wireless systems and networks, heterogeneous networks, and wireless transceiver design.

 

Chee Wei Tan

Associate Professor

City University of Hong Kong

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.

Biography:

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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