Hanan Samet (#/) received the B.S. degree in engineering from UCLA, and the M.S. Degree in operations research and the M.S. and Ph.D. degrees in computer science from Stanford University. At Stanford, he was a member of the Stanford Artificial Intelligence Lab where he was one of the developers of the SAIL programming language compiler. In 1975, he joined the Computer Science Department at the University of Maryland, College Park, where he is a Distinguished University Professor. He is a member of the Computer Vision Laboratory and leads a number of research projects on the use of hierarchical data structures for geographic information systems, computer graphics, image processing, and search. His research group has developed numerous systems including  QUILT system, SAND system, the SAND Browser, the VASCO spatial indexing applet, the MARCO, the STEWARD, the NewsStand, and TwitterStand systems. 

He is the founding editor-in-chief of the ACM Transactions on Spatial Algorithms and System (TSAS), an associate editor of Graphical Models and the IEEE Transactions on Computers, an advisory editor of the Journal of Visual Languages and Computing, and on the editorial boards of GeoInformatica and Image Understanding. He is the founding chair of the ACM SIG on Spatial Information (ACM SIGSPATIAL). He has served as the co-general chair of the 2007 and 2008 ACM SIGSPATIAL Conference on Geographic Information Systems (ACM GIS).

He is a Fellow of the IEEE, ACM, AAAS, IAPR, UCGIS, and was inducted to be a member of the ACM SIGGRAPH Academy. He was also elected to the ACM Council in 1989-1991where he served as the Capital Region Representative. He received the Walton Visitor Award from the Science Foundation of Ireland serving in the National University of Ireland at Maynooth, the 2010 University of Maryland College of Computer, Mathematical and Physical Sciences Board of Visitors Distinguished Faculty Award, the 2009 UCGIS research award, the 2011 ACM Paris Kanellakis theory and practice award, the 2014 IEEE Computer Society Wallace McDowell Award, best paper awards in the 2008 ACM SIGMOD and SIGSPATIAL Conferences, the 2012 SIGSPATIAL MobiGIS Workshop, and the 2007 Computer & Graphics Journal best paper. The 2008 ACM SIGSPATIAL best paper award winner also received the 2018 ACM SIGSPATIAL 10 Year Impact Award. He was elected to the ACM Council as the Capitol Region Representative for the term 1989-1991, and is an ACM Distinguished Speaker (2008-2015, and 2018-present).

His research interests include data structures, computer graphics, geographic information systems, computer vision, robotics, database management systems, and programming languages, and is the author of over 400 publications on these topics. 

Professor Wanlei Zhou is currently the Head of School of Computer Science in University of Technology Sydney (UTS). He received the B.Eng and M.Eng degrees from Harbin Institute of Technology, Harbin, China in 1982 and 1984, respectively, and the PhD degree from The Australian National University, Canberra, Australia, in 1991, all in Computer Science and Engineering. He also received a DSc degree (a higher Doctorate degree) from Deakin University in 2002. 

Before joining UTS, Professor Zhou held the positions of Alfred Deakin Professor, Chair of Information Technology, and Associate Dean (International Research Engagement) of Faculty of Science, Engineering and Built Environment, Deakin University. Professor Zhou has been the Head of School of Information Technology twice (Jan 2002-Apr 2006 and Jan 2009-Jan 2015) and Associate Dean of Faculty of Science and Technology at Deakin University (May 2006-Dec 2008). Professor Zhou also served as a lecturer in University of Electronic Science and Technology of China, a system programmer in HP at Massachusetts, USA; a lecturer in Monash University, Melbourne, Australia; and a lecturer in National University of Singapore, Singapore.

His research interests include security and privacy, bioinformatics, and e-learning. Professor Zhou has received over 10 ARC grants in the last 10 years and has published more than 400 papers in refereed international journals and refereed international conferences proceedings, including many articles in IEEE transactions and journals. He has also chaired many international conferences, including TrustCom, ISPA, IUCC, CSS, ICA3PP, EUC, NSS, HPCC, PRDC, etc., and has been invited to deliver keynote address in a number of international conferences, including SKG, NSS, PDCAT, NSS, EUC, ICWL, CIT, ISPA, ICA3PP, etc. Prof Zhou is a Senior Member of the IEEE.

Recent advances in Artificial Intelligence (AI) have led to a new era of Machine Learning techniques such as Deep Learning. This has attracted the interest of the networking community to try to take advantage of these novel techniques to develop a new breed of network operation techniques: models, management and optimization. However and in this context, ML applied to networking has not fulfilled its high expectations yet. The main reason behind this is that existing proposals typically use Fully-Connected Neural Networks (FCNN) that are not designed to learn computer networks. Indeed, data networks are fundamentally represented as graphs (topology, routing), and FCNN are not designed to learn information structured as graphs. Graph Neural Network (GNN) is a new family of neural networks that have been recently proposed with the aim of learning graphs. In other words, GNNs facilitate the learning of relations between entities in a graph and the rules for composing them (i.e., they have a strong relational inductive bias). In general, this is achieved by using Message-passing Neural Networks (MPNN), which have been used in the literature to develop new chemistry compounds. In this talk we will present RouteNet, a pioneering GNN designed to learn the complex relationship between routing, topology, traffic and the resulting delay, jitter and drops. We will also show that RouteNet is able to generalize, providing accurate estimates for unseen topologies, routings and traffics. Finally, we will discuss the prospects of this new technology and its potential in the field of Computer Networks.

ABDELHAMID MELLOUK is a full professor at University of Paris-Est (UPEC), Networks & Telecommunications (N&T) Department and Tinc-NET/LiSSi Laboratory France. He graduated in computer network engineering from the Computer Science High Eng. School, University Oran-EsSenia, Algeria, and the University of Paris Sud XI Orsay, received his Ph.D. in computer science from the same university, and a Doctorate of Sciences (Habilitation) diploma from UPEC. Founder of the Network Control Research activity with extensive international academic and industrial collaborations, his general area of research is in adaptive real-time bio-inspired control for high-speed new generation dynamic wired/wireless networking in order to maintain acceptable quality of service/experience for added value services. He is an active member of the IEEE Communications Society and held several offices including leadership positions in IEEE Communications Society Technical Committees. He has published/coordinated 11 books, 3 lecture notes and several refereed international publications in journals, conferences, and books, in addition to numerous keynotes and plenary talks in flagship venues. He serves on the Editorial Boards or as Associate Editor for several journals, and he is chairing or has chaired (or co-chaired) some of the top international conferences and symposia, including the TPS Chair of ICC 2017.

Masatoshi YOSHIKAWA is a Professor of Graduate School of Informatics at Kyoto University. He received the Dr. Eng. degree from Department of Information Science, Kyoto University in 1985. From April 2006, he has been a professor at Kyoto University. His current research interest includes privacy protection technologies, personal data market, and multi-user routing algorithms and services. He has published more than 200 papers refereed journal and conference papers. He is a General Co-Chair of the 6th IEEE International Conference on Big Data and Smart Computing (BigComp 2019). He is a PC member oaf IEEE ICDE 2020 and VLDB 2020. He is a member of Science Council of Japan (SCJ), ACM, IPSJ and IEICE.

Our research group is interested in why and how we are attracted to specific persons, content, and services. We have been analyzing such “attractiveness” by using big multimedia data. In this talk, I will briefly introduce some of our representative works that are related to ICTs such as presentation analysis, account promotion in SNSs, and so on.