Prof. Henry Leung
University of Calgary, Canada
SPIE Fellow, IEEE Fellow
Short Bio: Henry Leung is a professor of the Department of Electrical and Software Engineering of the University of Calgary. His current research interests include data analytic, information fusion, machine learning, signal and image processing, robotics, and internet of things. He has published over 350 journal papers and 250 refereed conference papers. Dr. Leung has been the associate editor of various journals such as the IEEE Circuits and Systems Magazine, International Journal on Information Fusion, IEEE Trans. Aerospace and Electronic Systems, IEEE Signal Processing Letters, IEEE Trans. Circuits and Systems, Scientific Reports He has also served as guest editors for the special issue “Intelligent Transportation Systems” for the International Journal on Information Fusion and “Cognitive Sensor Networks” for the IEEE Sensor Journal. He is the editor of the Springer book series on “Information Fusion and Data Science”. He is a Fellow of IEEE and SPIE.

Prof. Nasser Kehtarnavaz
University of Texas at Dallas, USA
SPIE Fellow, IEEE Fellow, AAIA Fellow
Speech Title: Mobile Edge AI: Machine Learning Solutions as Real-Time Smartphone Apps
Abstract: Edge computing solutions are expected to grow substantially during the next few years. This talk first covers the guidelines for turning deep learning models of intelligence into apps running in real-time on smartphones as edge devices. These guidelines are then applied to a real-time signal processing application and a real-time image processing application. The signal processing application involves machine learning-based personalization of the amplification function of hearing aids in an on-the-fly manner. The motivation behind this app is to use smartphones as edge devices to achieve better hearing over standard hearing aid prescriptions in the field or in real-world audio environments. The image processing application involves a deep learning solution to detect diabetic retinopathy in an on-the-fly manner as eye retina images are captured by smartphone cameras fitted with commercially available lenses. The motivation behind this app is to use smartphones as edge devices to conduct cost-effective and widely accessible first-pass eye exams in places with no access to fundus cameras.
Short Bio:Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor with the Department of Electrical and Computer Engineering and the Director of the Embedded Machine Learning Laboratory at The University of Texas at Dallas, Richardson, TX. His research areas include signal and image processing, machine learning, deep learning, and real-time implementation on embedded processors. He has authored or coauthored 11 books and over 400 publications in these areas. He is a Fellow of IEEE, a Fellow of SPIE, a Fellow of AAIA, a licensed Professional Engineer, and Editor-in-Chief of Journal of Real-Time Image Processing.

Prof. Changsheng Xu
Chinese Academy of Sciences, China
IEEE Fellow, IAPR Fellow
Short Bio: Dr. Xu is a Professor in National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences and Executive Director of China-Singapore Institute of Digital Media. His research interests include multimedia content analysis/indexing/retrieval, pattern recognition and computer vision. He has hold 30 granted/pending patents and published over 200 refereed research papers in these areas. Dr. Xu is an Associate Editor of IEEE Trans. on Multimedia, ACM Trans. on Multimedia Computing, Communications and Applications and ACM/Springer Multimedia Systems Journal. He received the Best Associate Editor Award of ACM Trans. on Multimedia Computing, Communications and Applications in 2012 and the Best Editorial Member Award of ACM/Springer Multimedia Systems Journal in 2008. He served as Program Chair of ACM Multimedia 2009. He has served as associate editor, guest editor, general chair, program chair, area/track chair, special session organizer, session chair and TPC member for over 20 IEEE and ACM prestigious multimedia journals, conferences and workshops. He is an ACM Distinguished Scientist, IEEE Fellow, and IAPR Fellow.

Prof. Zhu Han
University of Houston, USA
IEEE Fellow, AAAS Fellow
Speech Title: Mean Field Games Guided Machine Learning in Distributed Systems
Abstract: Mean field games (MFGs) deal with the study and analysis of differential games (DGs) with a large number of indistinguishable, rational, and heterogeneous players. These methodologies approximate the Nash equilibriums for DGs with symmetric interactions among players. In contrast with classical game theory, where players need to react to every other player separately, MFGs simplify the game by modeling the interaction of a representative player with the collective behavior of the other players. In this talk, we first discuss the basic concepts behind MFGs as well as their difference with classical game theory techniques. Then, we will introduce how MFG can be connected with Artificial Intelligence (AI). Specifically, we will connect MFGs with several popular AI techniques, such as evolutionary neural architecture search with MFG selection mechanism, joint server-selection and handover design for satellite-based federated learning using mean-field evolutionary approach, MFG guided deep reinforcement learning for task placement in cooperative multi-access edge computing. Finally, we conclude with the contributions and advantages that MFG can bring to AI.
Short Bio: Zhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002, he was an R&D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006, he was a Research Associate at the University of Maryland. From 2006 to 2008, he was an assistant professor at Boise State University, Idaho. Currently, he is a John and Rebecca Moores Professor in the Electrical and Computer Engineering Department as well as the Computer Science Department at the University of Houston, Texas. Dr. Han is an NSF CAREER award recipient of 2010, and the winner of the 2021 IEEE Kiyo Tomiyasu Award. He has been an IEEE fellow since 2014, an AAAS fellow since 2020, an IEEE Distinguished Lecturer from 2015 to 2018, and an ACM Distinguished Speaker from 2022-2025. Dr. Han is also a 1% highly cited researcher since 2017.

Prof. Lap-Pui Chau
The Hong Kong Polytechnic University, China
IEEE Fellow
Short Bio: Lap-Pui Chau received a Ph.D. degree from The Hong Kong Polytechnic University in 1997. He was with the School of Electrical and Electronic Engineering, Nanyang Technological University from 1997 to 2022. He is currently a Professor in the Department of Electronic and Information Engineering, The Hong Kong Polytechnic University. His current research interests include image and video analytics, intelligent transportation, human motion analysis, and metaverse. He is an IEEE Fellow. He was the chair of Technical Committee on Circuits & Systems for Communications of IEEE Circuits and Systems Society from 2010 to 2012. He was general chairs and program chairs for some international conferences. Besides, he served as associate editors for several IEEE journals and Distinguished Lecturer for IEEE BTS.

Prof. Zhongfei (Mark) Zhang
Binghamton University, State University of New York, USA
IEEE Fellow, IAPR Fellow, AAIA Fellow
Speech Title: Uncertainty Analysis for Deep Learning
Abstract: One significant obstacle to deploying deep neural network (DNN) models in real-world applications is that deep learning systems often break down in novel situations. Specifically, DNNs tend to yield unreliable predictive uncertainty estimates and make high-confident yet incorrect predictions when exposed to inputs drawn from unfamiliar distributions. Consequently, accurate predictive uncertainty analysis of DNNs is critical in many high-stake applications such as medical diagnosis, self-driving vehicles, and financial decision-making, where silent mistakes can lead to catastrophic consequences. In this talk, I will first introduce a novel uncertainty factorization model as a theoretical foundation for uncertainty analysis in general. Based on this model, I will then introduce a general and flexible framework for predictive uncertainty estimation with promising evaluation results in several out-of-distribution detection tasks on both vision and language datasets.
Short Bio:Zhongfei (Mark) Zhang is a professor at Computer Science Department, Binghamton University, State University of New York (SUNY), USA. He received a B.S. in Electronics Engineering (with Honors), an M.S. in Information Sciences, both from Zhejiang University, China, and a PhD in Computer Science from the University of Massachusetts at Amherst, USA. His research interests are in the broad areas of machine learning, data mining, computer vision, and pattern recognition, and specifically focus on multimedia/multimodal data understanding and mining. He was on the faculty of Computer Science and Engineering at the University at Buffalo, SUNY, before he joined the faculty of Computer Science at Binghamton University, SUNY. He is the author or co-author of the very first monograph on multimedia data mining and the very first monograph on relational data clustering. He has published over 200 papers in the premier venues in his areas. He holds more than twenty inventions, has served as members of the organization committees of several premier international conferences in his areas including general co-chair and lead program chair, and as editorial board members for several international journals. He served as a French CNRS Chair Professor of Computer Science at the University of Lille 1 in France, a JSPS Fellow in Chuo University, Japan, a QiuShi Chair Professor in Zhejiang University, China, as well as visiting professorships from many universities and research labs in the world when he was on leave from Binghamton University years ago. He received many honors including SUNY Chancellor’s Award for Scholarship and Creative Activities, SUNY Chancellor’s Promising Inventor Award, and best paper awards from several premier conferences in his areas. He is a Fellow of IEEE, IAPR, and AAIA.

Prof. Hui Yuan
Shandong University, China
IEEE Senior Member
Speech Title: Perceptual quality-based joint bit allocation between geometry and color for 3D dense point cloud
Abstract: In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with the perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and videos, no such one exists for point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization step sizes and whose coefficients can easily be computed from two features extracted from the original point cloud. Subjective quality tests with 400 compressed point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearson linear correlation coefficient. Moreover, we show that for the same target bitrate, rate-distortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric.
Short Bio: Hui Yuan (Senior Member, IEEE) received the B.E. and Ph.D. degrees in telecommunication engineering from Xidian University, Xi’an, China, in 2006 and 2011, respectively. In April 2011, he joined Shandong University, Ji’nan, China, as a Lecturer (April 2011–December 2014), an Associate Professor (January 2015-October 2016), and a Professor (September 2016). From January 2013 to December 2014, and from November 2017 to February 2018, he worked as a Postdoctoral Fellow (Granted by the Hong Kong Scholar Project) and a Research Fellow, respectively, with the Department of Computer Science, City University of Hong Kong. From November 2020 to November 2021, he worked as a Marie Curie Fellow (Granted by the Marie Skłodowska-Curie Actions Individual Fellowship under Horizon2020 Europe) with the School of Engineering and Sustainable Development, De Montfort University, Leicester, U.K. From October 2021 to November 2021, he also worked as a visiting researcher (secondment of the Marie Skłodowska-Curie Individual Fellowships) with the Computer Vision and Graphics group, Fraunhofer Heinrich-Hertz-Institut (HHI), Germany. His current research interests include 3D visual coding and communication. He served as an Area Chair for IEEE ICME 2023, ICME 2022, ICME 2021, IEEE ICME 2020, and IEEE VCIP 2020. He serves as a member of IEEE CTSoc Audio/Video Systems and Signal Processing Technical Committee (AVS TC) and APSIPA Image, Video, and Multimedia Technical Committee.

Prof. Ruili Wang
Massey University (Albany Campus), Auckland, New Zealand
Short Bio:Ruili Wang received the PhD degree in computer science from Dublin City University, Dublin, Ireland. He is currently a professor of artificial intelligence and the chair of research with the School of Natural and Computational Sciences, Massey University, Auckland, New Zealand, where he is the director of the Centre of Language and Speech Processing. His current research interests include speech processing, language processing, video processing, data mining, and intelligent systems. He is a member and an associate editor for the editorial boards for international journals, such as the journals of IEEE Transactions on Emerging Topics in Computational Intelligence, Knowledge and Information Systems, and Applied Soft Computing.