Prof. Dong Xu, The University of Hong Kong, Hong Kong, China
Fellow of IEEE and IAPR
Foreign Member of the Academia Europaea (The Academy of Europe)

Prof. Dong Xu is a Professor in the Computer Science Department, The University of Hong Kong, where he serves as the Director of the JC STEM Lab of Multimedia and Machine Learning. After receiving his PhD degree from University of Science and Technology of China in 2005, he worked as a postdoctoral research scientist at Columbia University, a tenure-track and tenured faculty member at Nanyang Technological University, and the Chair in Computer Engineering at The University of Sydney. Prof. Xu is an active researcher in the areas of computer vision, multimedia and machine learning. He was selected as a Clarivate Analytics Highly Cited Researcher twice in 2021 and 2018. He was also selected as an Australian Research Council Future Fellow (Level 3, Professorial Level) in 2018 and awarded the IEEE Computational Intelligence Society Outstanding Early Career Award in 2017. He has published more than 150 papers in IEEE Transactions and leading conferences including CVPR, ICCV, ECCV, ICML, ACM MM and MICCAI. His co-authored works (with his former PhD students) received the CVPR Best Student Paper Award in 2010 and the IEEE Transactions on Multimedia Prize Paper Award in 2014. He is/was on the editorial boards of ACM Computing Surveys (Senior Associate Editor since October 2022), IEEE Transactions including T-PAMI, T-IP, T-NNLS, T-CSVT and T-MM, and other five journals, as well as served as a guest editor of more than ten special issues in multiple journals (e.g., IJCV, IEEE/ACM Transactions). He will serve/served as the Program Coordinator of ACM Multimedia 2024, a steering committee member of ICME (2016-2017) and a Program Co-chair of five international conferences/workshops (e.g., ICME 2014). He was also involved in the organization committees of many international conferences and served as an area chair of leading conferences such as ICCV, CVPR, ECCV, ACM MM and AAAI. He received the Best Associate Editor Award of T-CSVT in 2017. He is a Fellow of IEEE and IAPR (The International Association for Pattern Recognition) and a Foreign Member of the Academia Europaea (The Academy of Europe).

Speech Title: Towards Impactful Research: From Visual Domain Adaptation to Deep Video Compression

Abstract: In this talk, I will first introduce our previous domain adaptation works, including our pioneering works in developing new domain adaptation (transfer learning) methods for video event recognition, and a series of subsequent works for single source domain adaptation, multi-domain adaptation, heterogeneous domain adaptation, domain generalization and deep domain adaptation, as well as their applications in various computer vision tasks. Then I will describe our previous deep video compression works, including the first end-to-end optimized deep video compression (DVC) framework, and our subsequent works including the feature-space video coding (FVC) network, as well as our recent works for coding mode prediction and stereo video compression.

Prof. Mugen Peng, Beijing University of Posts and Telecommunications, China
Fellow of IEEE

Mugen Peng received the Ph.D. degree in communication and information systems from the Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2005. Afterward, he joined BUPT, where he has been a Full Professor with the School of Information and Communication Engineering since 2012. In 2014, he was an Academic Visiting Fellow with Princeton University, Princeton, NJ, USA. He leads a Research Group focusing on wireless transmission and networking technologies with the State Key Laboratory of Networking and Switching Technology, BUPT. He has authored/coauthored over 100 refereed IEEE journal papers and over 300 conference proceeding papers. Dr. Peng is on the Editorial/Associate Editorial Board of the IEEE Communications Magazine, the IEEE INTERNET OF THINGS JOURNAL, and IEEE ACCESS. He was a recipient of the 2018 Heinrich Hertz Prize Paper Award, the 2014 IEEE ComSoc AP Outstanding Young Researcher Award, and the Best Paper Award in the JCN 2016 and IEEE WCNC 2015. He is the Fellow of IEEE.

Speech Title:Performance Analysis of Integrated Sensing and Communication Systems

Abstract: Compared to dedicated communication or sensing solutions, the integration of sensing and communication (ISAC) is promising to achieve advantages in terms of cost, power consumption, and hardware size, while fostering potential mutual gains. However, the studies on communication and sensing have historically been conducted independently and their interactions remain unclear. To address this situation, it is imperative to extend the ISAC theory and overcome the bottlenecks caused by the inconsistent evaluation metrics and unclear trade-off relationships in ISAC. This report introduces and analyzes the extended ISAC theory, addressing the issues mentioned above for various waveforms and frequencies, respectively. Specifically, the pilot-assisted communication and delay-Doppler channel calibration is developed and explained theoretically, separately leading to expanded ISAC performance limits for orthogonal frequency division multiplexing (OFDM) and orthogonal time-frequency apace (OTFS). Then, the trade-off and performance limits of ISAC for multiple frequencies are given individually, characterized by various critical technologies in the WIFI band, millimeter wave band, and terahertz band. Finally, some open issues lying in ISAC theory research are discussed as future research directions.

Prof. Konstantinos (Kostas) N. Plataniotis, University of Toronto, Canada
IEEE Fellow
Editor-in-Chief of IEEE Signal Processing Letters

Konstantinos (Kostas) N. Plataniotis received his B. Eng. degree in Computer Engineering from the University of Patras, Greece and his M.S. and Ph.D. in Electrical Engineering from Florida Institute of Technology Melbourne, Florida. Dr. Plataniotis is a Professor with The Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto in Toronto, Ontario, Canada, where he directs the Multimedia Laboratory and the University of Toronto-Huawei Mobile AI Laboratory. He holds the Bell Canada Endowed Chair in Multimedia since 2014. His research interests primarily include image/signal processing, machine learning and adaptive learning systems, visual data analysis, multimedia and knowledge media, and affective computing. Dr. Plataniotis is a Fellow of IEEE, a Fellow of the Engineering Institute of Canada, a Fellow of the Canadian Academy of Engineering / L’ Academie Canadienne Du Genie, and a registered professional engineer in Ontario.

He has served as the Editor-in-Chief of the IEEE Signal Processing Letters. He was the Technical Co-Chair of the IEEE 2013 International Conference in Acoustics, Speech and Signal Processing, and he served as the inaugural IEEE Signal Processing Society Vice President for Membership (2014 -2016) and General Co-Chair for the 2017 IEEE GLOBALSIP. He served as the 2018 IEEE International Conference on Image Processing (ICIP 2018) and General Co-Chair of the 2021 International Conference on Acoustics, Speech and Signal Processing (ICASSP21).  He will be the General Chair of the 2027 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2027).

Speech Title: Machine Learning in Engineering: Panacea or Deep Trouble?

Prof. Peng Shi, The University of Adelaide, Adelaide, Australia
IEEE Fellow, IET Fellow
Editor-in-Chief of IEEE Transactions on Cybernetics

Peng Shi received the PhD degree in Electrical Engineering from the University of Newcastle, Australia, the PhD degree in Mathematics from the University of South Australia, the Doctor of Science degree from the University of Glamorgan, UK, and the Doctor of Engineering degree from the University of Adelaide, Australia. He is now a Professor at the School of Electrical and Mechanical Engineering, and the Director of Advanced Unmanned Systems Laboratory, at The University of Adelaide, Australia. His research interests include systems and control theory and applications to autonomous and robotic systems, cyber-physical systems, and multi-agent systems. He received the Ramesh Agarwal Life-time Achievement Award in Science, Engineering and Technology from the International Engineering and Technology Institute in 2023, the MA Sargent Medal Award from Engineers Australia in 2022; the honor of Life-time Achiever Leader-Board and Field Leader from The AUSTRALIAN Research Review from 2019-2022, and the recognition of a Highly Cited Researcher from Thomson Reuters 2014-2022. Currently he serves as the Editor-in-Chief of IEEE Transactions on Cybernetics, a Senior Editor of IEEE Access, and an editorial member for a number of journals, including Automatica and IEEE Transactions on (Artificial Intelligence, and Circuits and Systems). His professional services also include as the President of the International Academy for Systems and Cybernetic Sciences, the Vice President of IEEE SMC Society, and IEEE SMC Society Distinguished Lecturer. He is a Fellow of IEEE, IET, IEAust and CAA, and a Member of the Academy of Europe.

Speech Title: Consensus and Formation Control for Multi-agent Systems

Abstract: The key features of Multi-agent Systems (MAS) are communication, coordination, and collaboration, by which the agents can achieve a common (and possibly difficult) goal in a more effective and efficient way. Three main topics within the realm of MAS are consensus, flocking and formation control. Cooperating processes often require agents to reach a consensus, which is the fundamental problem in MAS. Flocking (or swarming) is a self-organizing behavior originated from small-size animals with lower intelligence, which enables the emergence of swarm intelligence to improve the whole system survivability and competitiveness. Formation control generally aims to drive the agents to achieve a desired formation, scalable and/or changeable. In this talk, modeling analysis and design of a variety of distributed schemes for consensus and formation control are introduced. Simulations and experimental examples are provided to demonstrate the potential of the proposed new design techniques.

Prof. (Kit) Kai-Kit Wong (黃繼傑), University College London, UK
IEEE Fellow, IET Fellow
Full Professor and Chair in Wireless Communications
Editor-in-Chief for IEEE Wireless Communications Letters (2020-present)

(Kit) Kai-Kit Wong received the BEng, the MPhil, and the PhD degrees, all in Electrical and Electronic Engineering, from the Hong Kong University of Science and Technology (UST), Hong Kong, in 1996, 1998, and 2001, respectively. He moved to the UK after beginning his early career at the University of Hong Kong between 2001 and 2004, during which he also spent time at then AT&T Bell-Labs in Holmdel, New Jersy and Stanford University in the United States. Presently, he is Chair Professor of Wireless Communications at the Department of Electronic and Electrical Engineering, University College London (UCL), United Kingdom. He is Fellow of IEEE and IET. Since 2020, he has been the Editor-in-Chief for IEEE Wireless Communications Letters, and also the Subject Editor-in-Chief for Wireless Communications of IET Electronics Letters.

Speech Title: Bruce Lee Inspired Fluid Antenna Systems for 6G

Abstract: “Be formless … shapeless, like water!”, which were the words used by Bruce Lee, as he was revealing the philosophy of Jeet Kune Do, the martial arts system Lee founded in 1967. Many parallels can be drawn in wireless communications technologies where engineers have been seeking greater flexibility in using the spectral and energy resources for improving network performance. In this talk, I will speak on a novel antenna technology, referred to as fluid antenna, that adopts a software-controlled, position-flexible antenna to operate on the best signal envelope within a given space. This talk presents some early results on fluid antenna systems, which shows great promises on improving wireless communication performance.

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