Prof. Zhu Han University of Houston, USA
AAAS Fellow, IEEE Fellow

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 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 in Boise State University, Idaho. Currently, he is a John and Rebecca Moores Professor in Electrical and Computer Engineering Department as well as Computer Science Department at University of Houston, Texas. His research interests include security, wireless resource allocation and management, wireless communication and networking, game theory, and wireless multimedia. Dr. Han is an NSF CAREER award recipient of 2010. Dr. Han has several IEEE conference best paper awards, and winner of 2011 IEEE Fred W. Ellersick Prize, 2015 EURASIP Best Paper Award for the Journal on Advances in Signal Processing and 2016 IEEE Leonard G. Abraham Prize in the field of Communication Systems (Best Paper Award for IEEE Journal on Selected Areas on Communications). Dr. Han is the winner 2021 IEEE Kiyo Tomiyasu Award. He has been an IEEE fellow since 2014, AAAS fellow since 2020, IEEE Distinguished Lecturer from 2015 to 2018 and ACM Distinguished Speaker from 2022-2025. Dr. Han is a 1% highly cited researcher according to Web of Science since 2017.

(Online Talk) Speech Title: Federated Learning and Analysis with Multi-access Edge Computing for Connected and Automated Vehicles
Abstract: In recent years, mobile devices are equipped with increasingly advanced computing capabilities, which opens up countless possibilities for meaningful applications. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, multi-access edge computing (MEC) has been proposed to bring intelligence closer to the edge, where data is originally generated. However, conventional edge ML technologies still require personal data to be shared with edge servers. Recently, in light of increasing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train a local ML model required by the server. The end devices then send the local model updates instead of raw data to the server for aggregation. FL can serve as enabling technology in MEC since it enables the collaborative training of an ML model and also enables ML for mobile edge network optimization. However, in a large-scale and complex mobile edge network, FL still faces implementation challenges with regard to communication costs and resource allocation. In this talk, we begin with an introduction to the background and fundamentals of FL. Then, we discuss several potential challenges for FL implementation in connected and automated vehicles. In addition, we study the extension to Federated Analysis (FA) with data analysis in both user and MEC sides.

Prof. Haizhou Li, IEEE Fellow, ISCA Fellow
The Chinese University of Hong Kong, Shenzhen, China
Adjunct Professor at the National University of Singapore (NUS)

Haizhou Li is currently a Professor at The Chinese University of Hong Kong, Shenzhen, China, and an adjunct Professor at the National University of Singapore (NUS). Prior to that, he was the Principal Scientist and Department Head of Human Language Technology in the Institute for Infocomm Research, Singapore (2003-2016). Prof. Li’s research interests include speech information processing, natural language processing, and human-machine interface. Prof. Li has served as the Editor-in-Chief of IEEE/ACM Transactions on Audio, Speech and Language Processing (2015-2018), a Member of the Editorial Board of Computer Speech and Language (2012-2018), and a Member of IEEE Speech and Language Processing Technical Committee (2013-2015). He was the President of the International Speech Communication Association (ISCA, 2015-2017), the President of Asia Pacific Signal and Information Processing Association (2015-2016), and the President of Asian Federation of Natural Language Processing (2017-2018). He was the General Chair of ACL 2012, INTERSPEECH 2014, IEEE ASRU 2019, and ICASSP 2022. Prof. Li is a Fellow of the IEEE, a Fellow of ISCA, and a Fellow of Academy of Engineering Singapore. He was a recipient of the President’s Technology Award 2013 in Singapore. He was named one of the two Nokia Visiting Professors in 2009 by the Nokia Foundation, and U Bremen Excellence Chair Professor in 2019 by Bremen University, Germany.

(Online Talk) Speech Title: Detection of Auditory Attention in Human Brain
Abstract: Humans have the ability to selectively listen to one of the speakers in a multi-talk acoustic environment. This is also called the cocktail party effect or the selective auditory attention. In this talk, we will discuss the recent studies on how such selective auditory attention is reflected in brain signals, and the computational solutions to decode  brain activities from brain signals. The advances in brain signals decoding will enable innovative brain-computer interfaces such as smart hearing aids and mind-speaking prosthetics.

Prof. KWONG Tak Wu Sam
City University of Hong Kong, China
IEEE Fellow

Sam Kwong received his B.Sc. degree from the State University of New York at Buffalo, M.A.Sc. in electrical engineering from the University of Waterloo in Canada, and Ph.D. from Fernuniversität Hagen, Germany. Before joining the City University of Hong Kong (CityU), he was a Diagnostic Engineer with Control Data Canada. He was responsible for designing diagnostic software to detect the manufacturing faults of the VLSI chips in the Cyber 430 machine. He later joined Bell-Northern Research as a Member of Scientific Staff working on the Integrated Services Digital Network (ISDN) project.
Kwong is currently a Chair Professor at the CityU Department of Computer Science, where he previously served as Department Head and Professor from 2012 to 2018. Prof Kwong joined CityU as a lecturer in the Department of Electronic Engineering in 1989. Prof. Kwong is the associate editor of leading IEEE transaction journals, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Cybernetics.
Kwong is actively engaged in knowledge exchange between academia and industry. In 1996, he was responsible for designing the first handheld GSM mobile phone consultancy project at the City University of Hong Kong, one of the largest. He has filed more than 20 US patents, of which 13 have been granted.
Kwong has a prolific research record. He has co-authored three research books, eight book chapters, and over 300 technical papers. According to Google Scholar, his works have been cited more than 25,000 times with an h-index of 70. He has been the distinguished lecturer of IEEE SMCS since 2018 and delivers two DL lectures yearly to promote IEEE SMC Society and cutting-edge cybernetics technology. He also frequently delivers keynote speeches in IEEE supported conferences. In 2014, he was elevated to IEEE Fellow for his contributions to optimization techniques in cybernetics and video coding. He is also a fellow of Asia-Pacific Artificial Intelligence Association (AAIA) in 2022.
Kwong’s involvement in the multiple facets of IEEE has been extensive and committed throughout the years. For IEEE Systems, Man and Cybernetics Society (SMCS), he serves as Hong Kong SMCS Chapter Chairman, Board Member, Conference Coordinator, Membership Coordinator and Member of the Long Range Planning and Finance Committee, Vice President of Conferences and Meetings, Vice President of Cybernetics. He led the IEEE SMC Hong Kong Chapter to win the Best Chapter Award in 2011 and was awarded the Outstanding Contribution Award for his contributions to SMC 2015. He was the President-Elect of the IEEE SMC Society in 2021. Currently, he serves as the President of the IEEE SMC Society.

(Online Talk) Speech Title: Intelligent Video Coding by Data-driven Techniques and Learning Models
Abstract: In June 6th 2016, Cisco released the White paper[1], VNI Forecast and Methodology 2015-2020, reported that 82 percent of Internet traffic will come from video applications such as video surveillance, content delivery network, so on by 2020. It also reported that Internet video surveillance traffic nearly doubled, Virtual reality traffic quadrupled, TV grew 50 percent and similar increases for other applications in 2015. The annual global traffic will first time exceed the zettabyte(ZB;1000 exabytes[EB]) threshold in 2016, and will reach 2.3 ZB by 2020. It implies that 1.886ZB belongs to video data. Thus, in order to relieve the burden on video storage, streaming and other video services, researchers from the video community have developed a series of video coding standards. Among them, the most up-to-date is the High Efficiency Video Coding(HEVC) or H.266 standard, which has successfully halved the coding bits of its predecessor, H.264/AVC, without significant increase in perceived distortion. With the rapid growth of network transmission capacity, enjoying high definition video applications anytime and anywhere with mobile display terminals will be a desirable feature in the near future. Due to the lack of hardware computing power and limited bandwidth, lower complexity and higher compression efficiency video coding scheme are still desired. For higher video compression performance, the key optimization problems, mainly decision making and resource allocation problem, shall be solved. In this talk, I will present the most recent research and new developments on deep neural network based video coding and its applications such as saliency detection, perceptual visual processing and others. This is very different from the traditional approaches in video coding. We hope applying these intelligent techniques to vide coding could allow us to go further and have more choices in trading off between cost and resources.