Prof. James Tin-Yau KWOK, IEEE Fellow

Hong Kong University of Science and Technology, China 

Brief Introduction: Prof. Kwok is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. Prof. Kwok served / is serving as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing, Artificial Intelligence Journal, International Journal of Data Science and Analytics, and Editorial Board Member of Machine Learning. He also served/is serving as Senior Area Chairs of major machine learning / AI conferences including NeurIPS, ICML, ICLR, and IJCAI. He is recognized as the Most Influential Scholar Award Honorable Mention for "outstanding and vibrant contributions to the field of AAAI/IJCAI between 2009 and 2019". He is an IEEE Fellow, and will be the IJCAI-2025 Program Chair.

Speech Title: Diffusion Models for Time Series Forecasting

Abstract: Diffusion models have led to significant breakthroughs in the generation of images, audio and text. Recently, there have been attempts on extending the diffusion model for time series data. However, it is still an open question on how to adapt their strong modeling ability to model time series. In this talk, we introduce non-autoregressive diffusion models that achieve high-quality time series prediction. As different patterns are usually exhibited at multiple scales of a time series, we leverage the multi-resolution temporal structure and propose the multi-resolution diffusion model (mr-Diff). By using the seasonal-trend decomposition, fine-to-coarse trends are sequentially extracted from the time series for forward diffusion. The denoising process then proceeds in an easy-to-hard non-autoregressive manner. The coarsest trend is generated first. Finer details are progressively added, using the predicted coarser trends as condition variables. Experimental results on real-world time series datasets demonstrate that mr-Diff outperforms state-of-the-art time series diffusion models. It is also better than or comparable across a wide variety of advanced time series prediction models. 

Prof. Shahram Latifi, IEEE Fellow

University of Nevada,USA

Brief Introduction: Shahram Latifi, an IEEE Fellow, received the Master of Science egree in Electrical Engineering from Fanni, Teheran University, Iran in 1980. He received the Master of Science and the PhD degrees both in Electrical and Computer Engineering from Louisiana State University, Baton Rouge, in 1986 and 1989, respectively. He is currently a Professor of Electrical Engineering at the University of Nevada, Las Vegas. Dr. Latifi is the director of the Center for Information and Communication Technology (CICT) at UNLV. He has designed and taught graduate courses on Bio-Surveillance, Image Processing, Computer Networks, Fault Tolerant Computing, and Data Compression in the past twenty years. He has given seminars on the aforementioned topics all over the world. He has authored over 200 technical articles in the areas of image processing, biosurveillance, biometrics, document analysis, computer networks, fault tolerant computing, parallel processing, and data compression. His research has been funded by NSF, NASA, DOE, Boeing, Lockheed and Cray Inc. Dr. Latifi was an Associate Editor of the IEEE Transactions on Computers (1999-2006) and Co-founder and General Chair of the IEEE Int'l Conf. on Information Technology. He is also a Registered Professional Engineer in the State of Nevada.

Speech Title: The Dual Horizon - Navigating Hope and Fear in the Digital Age

Abstract: The advancement in Artificial Intelligence (AI) has been accelerating at a phenomenal rate since the onset of the 21st century.  Empowered by powerful machines and huge data sets, the AI is arguably considered the next industrial revolution with the biggest impact among other revolutions we have seen in the past few centuries.  As is the case with any new technology, the AI is a double-edged sword that is disrupting the way we live our lives in the most profound way.  While AI is promising to find cure for cancer, solve climate change, predict natural clamities and alleviate the global poverty,  it is threatening to jeopardize the civilization in a much more profound way than ever before.  Unemployment,  privacy and ethical violations,  autonomous weapons and more importantly, the machines taking control of the societies are looming.  In this talk, I will briefly review the past and current status of the AI technology and present  some of the deficiencies and vulnerabilities of the AI as we see them today.  In addition, we offer a futuristic view of what the AI may have in store for us over the next few decades.

Prof. P. Takis Mathiopoulos, IEEE Senior Member

University of Athens, Greece

Brief Introduction: P. Takis Mathiopoulos received the Ph.D. degree in digital communications from the University of Ottawa, Ottawa, Canada, in 1989. From 1982 to 1986, he was with Raytheon Canada Ltd., working in the areas of air navigational and satellite communications.

In 1989, he joined the Department of Electrical and Computer Engineering (ECE), University of British Columbia (UBC), Vancouver, Canada, as an Assistant Professor and where he was a faculty member until 2003, holding the rank of Professor from 2000 to 2003. From 2000 to 2014, he was the Director (2000 - 2004) and then the Director of Research of the Institute for Space Applications and Remote Sensing (ISARS), National Observatory of Athens (NOA). Since 2014, he is Professor of Telecommunications at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece. He also held visiting faculty long term honorary academic appointments as Guest Professor at South West Jiao Tong University (SWJTU), Chengdu, China, and Guest (Global) Professor at Keio University, Tokyo, Japan.  

His research activities and contributions have dealt with wireless terrestrial and satellite communication systems and network as well as in remote sensing, LiDAR systems, and information technology, including blockchain systems. In these areas, he has coauthored 140 journal papers published mainly in various IEEE journals, 1 book (edited), 5 book chapters, and more than 140 conference papers. Dr. Mathiopoulos has been or currently serves on the editorial board of several archival journals, including the IET Communications as an Area Editor, the IEEE Transactions on Communications, the Remote Sensing Journal, and as Specialty Chief Editor for the Arial and Space Network Journal of Frontiers.

From 2001 to 2014, he has served as a Greek Representative to high-level committees in the European Commission and the European Space Agency. He has been a member of the Technical Program Committees (TPC) for numerous IEEE and other international conferences and has served as TPC Vice Chair of several IEEE conferences. He has delivered numerous invited presentations, including plenary and keynote lectures, and has taught many short courses all over the world. As a faculty member UBC, he has been awarded an Advanced Systems Institute (ASI) Fellowship as well as a Killam Research Fellowship. He is also the co-recipient of two best conference paper awards and has received by the IEEE Communication Society the Satellite and Space Communication Technical Committee “2017 Distinguished Service Award” for outstanding contributions in the field of Satellite and Space Communications. 

Speech Title: (Optimal) Detection for (Fast) Fading Channels

Abstract: We will be reviewing the most important fading channel models and then present the most important associated receiver structures used in wireless digital communication systems. Emphasis will be given to the maximum likelihood sequence estimation receiver which lead to the so-called multiple differential detection (MDD) receiver structure for fast fading channels. Then we will be presenting the most important diversity receiver techniques and their application to “new” classes of statistical fading channels, such as the Weibull (short-term fading), the Lognormal (long-term fading) and the Generalized-K (composite fading) models. We will also be discussing some open research problems promising for future investigation. 

Prof. Li Chen, IEEE Senior Member

Sun Yat-sen University, China

Brief Introduction: Li Chen was awarded his PhD by Newcastle University in 2008 and now is a Professor of the School of Electronics and Information Engineering, Sun Yat-sen University. From Aug. 2017 to Mar. 2020, he was the Deputy Dean of the School of Electronics and Communication Engineering. He specializes in channel coding, particularly in algebraic coding theory and techniques. From Jul. 2015 to Jun. 2016, he took sabbatical, visiting Ulm University in Germany and University of Notre Dame in U.S. He has also visited the Institute of Network Coding, the Chinese University of Hong Kong for several occasions. 

He is a member of the IEEE Information Theory Society Board of Governors Conference Committee and chairing the Conference Committee. He founded and chairs the IEEE Information Theory Society Guangzhou Chapter, which was awarded 2021 Chapter of the Year of the IEEE Information Theory Society. He was awarded The Chinese Information Theory Young Researcher award by the Chinese society of Electronics. He is an Associate Editor of the IEEE Transactions on Communications. He has been organizing several international conferences and workshops, including the 2018 IEEE Information Theory Workshop (ITW) in Guangzhou and the 2022 IEEE East Asian School of Information Theory (EASIT) in Shenzhen, for which he is the General Co-Chair. He is also the TPC Co-Chair of the 2022 IEEE/CIC International Conference on Communications in China (ICCC) in Foshan. He will organize ISIT 2026 in Guangzhou China, which is the first time that ISIT lands on mainland China.

Speech Title: U-UV Codes -- The Good Short-to-Medium Length Codes

Abstract: Competent short-to-medium length channel codes will play an important role in future communication systems. This talk introduces a good performing short-to-medium length code: the UUV codes. U-UV codes are constructed by a number of component codes in the (U | U + V) recursive structure, where the U codes and V codes are component codes. This construction is known as the Plotkin construction and the generalized concatenated codes with inner polar codes. Good performing U-UV codes can be designed for a targeted transmission rate. The successive cancellation list (SCL) decoding of the U-UV codes is substantiated by the list decoding of the component codes. This talk also shows that SCL decoding of U-UV codes can provide competent error-correction performance, and they can outperform other competent short-to-medium length codes.

Prof.Guojun Han, IEEE Senior Member

Guangdong University of Technology, China

Brief Introduction: Prof. Guojun HAN received his Ph.D. from Sun Yatsen University, Guangzhou, China, and the M.E. degree from South China University of Technology, Guangzhou, China. From March 2011 to August 2013, he was a Research Fellow at the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. From October 2013 to April 2014, he was a Research Associate at the Department of Electrical and Electronic Engineering, Hong Kong University of Science and Technology. He is now a Full Professor and Dean at the School of Information Engineering, Guangdong University of Technology, Guangzhou, China. 

He has been a Senior Member of IEEE since 2014. His research interests are in the areas of memory devices and data storage systems, wireless communications, coding and information theory. He has more than 15 years’ experience on research and development of advanced channel coding and signal processing algorithms and techniques for various data storage and communication systems.

Speech Title: Resource Management for Caching, Communication, and Computing in Vehicular Edge Systems

Abstract: Effective resource management is pivotal in enhancing transmission efficiency across vehicular networks, particularly in the context of burgeoning multimedia content, computational demands, and latency-sensitive applications. Amidst the challenges of demand uncertainty, high dynamism, and task diversity, the complexity of resource management escalates, necessitating more advanced strategies. This presentation will delve into the evolution of technology standards within vehicular networks, shedding light on the ongoing progress in content distribution and computational task offloading within these systems. Our efforts encompass a dual-layer content caching strategy,  designed to optimize transmission latency and enhance hit rates for content access. Moreover, we introduce a dynamic caching and offloading strategy tailored for program-associated tasks, alongside an offloading and resource allocation strategy specifically catered to collaborative sensing tasks. These two strategies synergistically refine resource management, operating at a systemic macro perspective and a multi-agent collaboration level, respectively. Furthermore, we have developed a prototype of an object-level cooperative perception system, promoting the practical implementation of cooperative perception in vehicular environments.

Prof. Yong Yue, IET Fellow

Xi'an Jiaotong-Liverpool University, China

Brief Introduction: Yong Yue (BEng Northeastern China, PhD Heriot-Watt UK, CEng, FIET, FIMechE, FHEA) is currently a Professor and Director of the Virtual Engineering Centre (VEC). He was Head of Department of Computer Science and Software Engineering (2013-2019). Prior to joining XJTLU, he had held various positions in industry and academia in China and the UK, including Engineer, Project Manager, Professor, Director of Research and Head of Department. 

Professor Yue has experience in learning and teaching, research and enterprise as well as management. He has led a variety of research and professional projects supported by major funding bodies and industry. He has also lead curriculum development at both undergraduate and postgraduate levels. His Research interests include computer vision, robotics, virtual reality and operations research. He has over 250 peer-reviewed publications and supervised 27 PhD students to successful completion.

Speech Title: Intelligent Real-Time Path Planning for Unmanned Surface Vehicle

Abstract: Unmanned Surface Vehicles (USVs) play a key role in water environment monitoring. The backbone of USVs is intelligent path planning which is crucial for ensuring the safety, reliability and success of USVs amid challenges such as fluctuating currents and tides while detecting and avoiding obstacles. This talk will briefly introduce the USV, reviews contemporary techniques for path planning and present ongoing work on intelligent real-time path planning for the USV for water environment monitoring. The work covers a novel path-keeping algorithm based on artificial potential field (PK-APF), enhancing the USV ability to maintain its pre-set path under variable wind conditions; a novel riverbank following planner (RBFP) with point cloud data to realise autonomous navigation along riverbanks for surveying water environments; a self-supervised framework for autonomous USV docking without the need for traditional human labelling and camera calibration, leading to highly precise USV docking manoeuvres.

Prof. Weibin Wu

South China Agricultural University, China

Brief Introduction: Prof. Weibin Wu graduated from South China Agricultural University, majoring in Agricultural Mechanization Engineering. He completed his postdoctoral work at South China University of Technology in 2009. In 2015, he visited Washington State University as a national government-sponsored scholar. 

Currently, he is a professor and doctoral advisor at the College of Engineering, South China Agricultural University. He serves as Vice Chairman of the Automotive Electronics Branch of the Guangdong Electronic Society, a Director of the Guangdong Mountain Orchard Machinery Engineering Technology Research Center, and a core member of the first batch of technology experts in the Sanshui District of Foshan City. 

Additionally, he is a mechanization specialist at the Guangdong Tea Industry Technology System, Vice President of the Guangdong Electronic Information Industry Association, and a member of the Academic Committee of the Ministry of Industry and Information Technology Key Laboratory.

Prof. Wu has led more than 20 provincial and national research projects. His research focuses on fruit and tea garden intelligent mechanization equipment, the application of mechatronics technology in agriculture, and plug-in hybrid vehicles. His research has resulted in over 100 published papers and 26 authorized patents as the first inventor. 

He has won 16 provincial and ministerial research awards, including third prize in Guangdong Science and Technology, China Agricultural Machinery Science and Technology, and China Society of Automotive Engineers Science and Technology Progress, and a first prize in the China Commerce Federation Science and Technology Awards. In 2024, he was awarded the second prize in the China Electronics Society Science and Technology Awards.

Speech Title: Research progress on key technologies of an intelligent tea-picking robot

Abstract: Traditional and manual methods are time and labor-consuming and non-cost-effective in harvesting high-quality tea leaves. So, the development of automated tea-picking robots is crucial for the sustainable development of the tea industry. Machine vision and related equipment have significant potential in advanced agricultural applications. This speech reviews the different characteristics of tea-picking machines, their applications, and the research progress in tea harvesting domestically and internationally. However, precise identification through machine vision still faces many technical challenges, making it difficult for most robots to achieve real commercial applications. We focus on the key technologies of precise detection in intelligent tea-picking robots, analyze the various complex environments in tea gardens, and discuss the characteristics of tea leaves in the field of vision. An effective structure based on the fusion feature was designed, and the role of attention mechanisms and loss functions were analyzed to detect tea leaves within the vision field precisely. Finally, prospects and recommendations for developing intelligent tea-picking robots are proposed.