Prof. Jiangchuan Liu
IEEE Fellow
Simon Fraser University, Canada

Jiangchuan Liu is a Full Professor in the School of Computing Science, Simon Fraser University, British Columbia, Canada. He is a Fellow of The Canadian Academy of Engineering, an IEEE Fellow, and an NSERC E.W.R. Steacie Memorial Fellow. In the past he worked as an Assistant Professor at The Chinese University of Hong Kong, a research fellow at Microsoft Research Asia, and an EMC-Endowed Visiting Chair Professor of Tsinghua University.

He received the BEng degree (cum laude) from Tsinghua University, Beijing, China, in 1999, and the PhD degree from The Hong Kong University of Science and Technology in 2003, both in computer science. He is a co-recipient of the inaugural Test of Time Paper Award of IEEE INFOCOM (2015), ACM SIGMM TOMCCAP Nicolas D. Georganas Best Paper Award (2013), ACM Multimedia Best Paper Award (2012), and IEEE MASS Best Paper Award (2021).

His research interests include multimedia systems and networks, cloud and edge computing, social networking, online gaming, and Internet of things/RFID/backscatter. He has served on the editorial boards of IEEE/ACM Transactions on Networking, IEEE Transactions on Network Sciences and Engineering, IEEE Transactions on Big Data, IEEE Transactions on Multimedia, IEEE Communications Surveys and Tutorials, and IEEE Internet of Things Journal. He is a Steering Committee member of IEEE Transactions on Mobile Computing and Steering Committee Chair of IEEE/ACM IWQoS (2015-2017). He was TPC Co-Chair of IEEE INFOCOM'2021 and General Co-Chair of INFOCOM’2024.

Prof. Shui Yu
IEEE Fellow
University of Technology Sydney, Australia

Shui Yu (IEEE F’23) obtained his PhD from Deakin University, Australia, in 2004. He is a Professor of School of Computer Science, Deputy Chair of University Research Committee, University of Technology Sydney, Australia. His research interest includes Cybersecurity, Network Science, Big Data, and Mathematical Modelling. He has published five monographs and edited two books, more than 500 technical papers at different venues, such as IEEE TDSC, TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. His current h-index is 71. Professor Yu promoted the research field of networking for big data since 2013, and his research outputs have been widely adopted by industrial systems, such as Amazon cloud security. He is currently serving the editorial boards of IEEE Communications Surveys and Tutorials (Area Editor) and IEEE Internet of Things Journal (Editor). He served as a Distinguished Lecturer of IEEE Communications Society (2018-2021). He is a Distinguished Visitor of IEEE Computer Society, and an elected member of Board of Governors of IEEE VTS and ComSoc, respectively. He is a member of ACM and AAAS, and a Fellow of IEEE.

Prof. Minghua Chen
IEEE Fellow
City University of Hong Kong, Hong Kong, China

Minghua received his B.Eng. and M.S. degrees from the Department of Electronic Engineering at Tsinghua University. He received his Ph.D. degree from the Department of Electrical Engineering and Computer Sciences at University of California Berkeley. He is now a Professor at School of Data Science, City University of Hong Kong. Minghua received the Eli Jury award from UC Berkeley in 2007 (presented to a graduate student or recent alumnus for outstanding achievement in the area of Systems, Communications, Control, or Signal Processing) and The Chinese University of Hong Kong Young Researcher Award in 2013. He also received several best paper awards, including IEEE ICME Best Paper Award in 2009, IEEE Transactions on Multimedia Prize Paper Award in 2009, ACM Multimedia Best Paper Award in 2012, and IEEE INFOCOM Best Poster Award in 2021. He serves as Associate Editor of IEEE/ACM Transactions on Networking in 2014 - 2018. He is currently a Senior Editor for IEEE Systems Journal (2021- present), an Area Editor of ACM SIGEnergy Energy Informatics Review (2021 - present), and an Award Chair and Executive Committee member of ACM SIGEnergy (2018 - present). Minghua’s recent research interests include online optimization and algorithms, machine learning in power system operation, intelligent transportation systems, distributed optimization, delay-constrained network coding, and capitalizing the benefit of data-driven prediction in algorithm/system design. He is an ACM Distinguished Scientist and an IEEE Fellow.

Prof. Tahar Kechadi
University College Dublin, Ireland

Professor M-Tahar Kechadi obtained PhD and MSc degrees - in Computer Science from the University of Lille 1, France. He is currently a Professor of Computer Science at the School of Computer Science, UCD. He is a PI at the Insight Centre for Data Analytics. He serves on the scientific committees for several international conferences and organised and hosted one of the leading conferences in his area. The core and central focus of his research for the last decade is how to manage and analyse data quickly and efficiently. We live in the digital world and produce more data than we can analyse and exploit. This “big data” will continue to grow exponentially, underpin new waves of innovation in nearly every sector of the world economy, and reshape the framework we build and use computers (hardware and software). Currently, my research interests are primarily in
• Big Data Analytics and its applications to real-world applications (Healthcare and Digital Agriculture).
• Digital Forensics and Cybersecurity.
In big data applications, data analytics and computing environments created new problems and challenges for efficient execution and optimal system performance. These brought me to look at the challenges of data analytics in the heterogeneous, complex, distributed environment. Another key objective is to design and develop data analytics techniques that delineate a careful division of work between the user and the computer. One way to tackle this challenge is to provide constant feedback to the users and engage with them only when required. And finally, the scalability and privacy issues, as the datasets are becoming huge, containing data of various types about different systems or users. I am recently looking at these issues from a cybersecurity and data privacy point of view.