Keynote Speakers ISCEIC 2026
Prof. Kenli Li is Vice President of Hunan University, Director of the National Supercomputing Center in Changsha, a Fellow of CCF (China Computer Federation), a Baosteel Outstanding Teacher, an Elsevier Highly Cited Chinese Researcher, and a recipient of the Special Government Allowance of the State Council. He is the head of the Ministry of Education's Discipline Innovation and Talent Introduction Base for High-Efficiency Computing, and Director of the Ministry of Education Engineering Research Center for High-Performance Computing Application Software Technology. He serves as Vice Chairman of the National Supercomputing Innovation Alliance, a member of the Expert Committee of the New Generation Artificial Intelligence Industry Technology Innovation Alliance, a member of the Overall Expert Group of the National Key R&D Program for High-Performance Computing, an Associate Editor of IEEE-TC/TSUSC/TII, and an editorial board member ofJournal of Computer Research and Development. He has led more than 10 national and provincial/ministerial projects, including the National Natural Science Foundation of China Innovation Research Group Project, JWKJW key projects, and the Ministry of Education Discipline Breakthrough Program. He has received the Second Prize of the National Science and Technology Progress Award, the China Patent Gold Award, and the First Prize of the National Science and Technology Progress Award (Innovation Team Award, ranked 13th). His main research areas are high-performance intelligent computing software and applications, and he has published more than 150 papers in IEEE/ACM transactions. He holds more than 90 authorized patents and software copyrights.
TBD...
Chang Wen Chen is currently Chair Professor of Visual Computing at The Hong Kong Polytechnic University. Before his current position, he served as Dean of the School of Science and Engineering at The Chinese University of Hong Kong, Shenzhen from 2017 to 2020, and concurrently as Deputy Director at Peng Cheng Laboratory from 2018 to 2021. Previously, he has been an Empire Innovation Professor at the State University of New York at Buffalo (SUNY) from 2008 to 2021 and the Allan Henry Endowed Chair Professor at the Florida Institute of Technology from 2003 to 2007. He received his BS degree from the University of Science and Technology of China in 1983, his MS degree from the University of Southern California in 1986, and his PhD degree from the University of Illinois at Urbana Champaign (UIUC) in 1992.
He has served as an Editor-in-Chief for IEEE Trans. Multimedia (2014-2016) and IEEE Trans. Circuits & Systems for Video Technology (2006-2009). He has received many professional achievement awards, including 11 Best Paper Awards in various journals and conferences, the prestigious Alexander von Humboldt Award in 2010, the SUNY Chancellor’s Award for Excellence in Scholarship and Creative Activities in 2016, the UIUC ECE Distinguished Alumni Award in 2019, and the ACM SIGMM Outstanding Technical Achievement Award in 2024. He is an IEEE Fellow (2005), a SPIE Fellow (2007), and a Member of Academia Europaea (2021).
This talk shall focus on contemporary visual computing research trends with critical implications for 6G semantic communications. Semantic communication was initially proposed by Weaver and Shannon 70+ years ago in the early 1950s in which they outlined the classical definition of three levels of communications: the technical problem, the semantic problem, and the effectiveness problem. Until 5G, most researchers and practitioners have been working on the first technical problem. For 6G, semantic communication becomes necessary to handle the overwhelming volume of visual data among all IP traffic. We firmly believe that a paradigm-shifting framework needs to be designed to transport the volumetric visual data under the 6G mobile communication architecture. We show that recent technical advances in contemporary visual computing bear great potential for 6G semantic communication. Among the volumetric visual data, a significant portion of them has been acquired for machine intelligence purposes. Therefore, structured extraction and representation of the semantics from these visual data are desired to facilitate the 6G semantic communication. For contemporary visual computing, the well-structured scene graph generation (SGG) approaches have been demonstrated capable of representing compactly the logical relationship among the subjects and objects detected from the visual data. We shall show that the unique capability of structured SGG can be applied to 6G semantic communication towards future advances in integrating visual computing with 6G.
Professor Yuan Yan Tang is currently the Director of the Smart City Research and Development Center at the Institute for Advanced Studies, University of Macau (Hengqin), Chair Professor Emeritus at the University of Macau, and Chair Professor Emeritus at Hong Kong Baptist University. He previously served as an Honorary Lecturer at the University of Hong Kong, a Visiting Professor and Researcher at the Centre for Pattern Analysis and Machine Intelligence at Concordia University, Canada, Dean of the School of Computer Science at Chongqing University, and Chair Professor and Dean of the Faculty of Science and Technology at the University of Wollongong (Hong Kong Campus), Australia. Professor Tang is a reviewer, deputy group leader, and group leader of the Information Science Group II of the National Natural Science Foundation of China, a reviewer for the National Science Fund for Distinguished Young Scholars, and a reviewer for the Yangtze River Scholars Program of the Ministry of Education. He is the Founding Vice Chairman of the Hong Kong University Students' Association for Information Technology and Management and a Founding Director of the Hong Kong Maritime University Students' Association. He has received one first prize of the Natural Science Award from the Ministry of Education of China and five second and third prizes of the Natural Science Award from the Macau Special Administrative Region and Chongqing Municipality. He holds 7 Chinese patents, and his research findings have been applied to 15 US patents and 2 European patents. He has been invited to give academic reports at the Chinese Academy of Sciences, the National Natural Science Foundation of China, the General Office of the CPC Central Committee, over 50 universities, and in more than 10 countries including the United States, the United Kingdom, and Canada.
Professor Y. Y. Tang has long been engaged in research on artificial intelligence, pattern recognition, image processing, and Chinese computer processing. He is an internationally renowned expert in artificial intelligence and has made significant contributions to the development of this field.
Internationally, Professor Tang is a founder of the interdisciplinary field of wavelet analysis and artificial intelligence, leading the development of this innovative field. His English monographs, “Wavelet Theory and its Application to Pattern Recognition” (2000) and “Wavelet Theory Approach to Pattern Recognition” (2009, 3rd edition, 2025), are considered classic works in this field. Internationally, they are praised as "the world's first monograph systematically discussing wavelet analysis and pattern recognition, laying the foundation for this interdisciplinary field." He also founded a series of international conferences in this discipline and has served as chairman and chair of the steering committee on multiple occasions. He founded the first top-tier international academic journal on wavelets and their applications, an SCI-indexed journal, and served as its editor-in-chief, developing the interdisciplinary field of wavelet theory and artificial intelligence into a mainstream research direction globally. Professor Tang pioneered a new direction in artificial intelligence combining entropy theory and fractal theory, establishing the theoretical framework for this field. His findings were included in the English-language “Handbook of Pattern Recognition”, becoming a key part of modern pattern recognition theory.
In 2002, with the support of the Hong Kong SAR government (then Chief Executive Tung Chee-hwa personally wrote to the International Association for Pattern Recognition for support), he led the bidding team to defeat the United States, Germany, Japan, and other countries to win the right to host the “International Conference on Pattern Recognition”. China successfully hosted the conference in 2006, serving as its chairman. This was the first time China hosted the International Conference on Pattern Recognition, bringing international honor to my country's pattern recognition community.
Y. Y. Tang has published over 600 international academic papers (over 400 of which are SCI-indexed) and 25 English monographs and books.
In the 2020-2026 global ranking of computer scientists, Professor Tang ranked first in Macau and is a highly cited computer scientist.
Artificial intelligence (AI) has developed rapidly in recent years and has been widely applied. AI involves many theoretical issues, among which the representation of knowledge is a crucial and core problem. This report provides a clear and concise introduction to several knowledge representation methods in artificial intelligence. It introduces six major categories of knowledge representation methods, each with its own strengths, which together constitute the "cognitive system" of AI: Logical Representation, Production Rules, Semantic Networks, Frame Representation, Ontology, Knowledge Graphs, and Knowledge Representation for Generative AI.
Dr. Quan Xue is a Professor and Doctoral Supervisor, an IEEE Fellow, and a national overseas high-level expert. He served as a Lecturer to Professor at the University of Electronic Science and Technology of China (1993–2001), a Research Fellow at The Chinese University of Hong Kong (1997–1998), and an Associate Professor, Professor, Chair Professor, and Associate Vice-President at City University of Hong Kong (1999–2017). From 2017 to 2026, he served as Dean of the School of Electronic and Information Engineering at South China University of Technology (SCUT); since 2020, he has served as Dean of the School of Integrated Circuits at SCUT; from 2020 to 2023, he served as Chief Antenna Expert at Huawei’s 2012 Laboratories; and since 2020, he has been a member of the Discipline Evaluation Group of the State Council Academic Degrees Committee. He has long been engaged in research on microwave and millimeter-wave integrated circuits and antennas. He has published more than 600 SCI papers and obtained more than 100 Chinese patents and more than 40 U.S. patents. He has received the Second Prize of the National Technological Invention Award, the Guangdong Provincial Science and Technology Award, and other awards, as well as the 2017 IEEE APS Wheeler Award. His achievements have been mainly applied in new-generation communications, satellite communications, wireless sensing, navigation, and other fields.
Leveraging the excellent mechanical stability, electromagnetic adaptability, and ultralight properties of paper honeycomb, we propose a new 4D antenna paradigm for radiation empowerment. In addition to the honeycomb’s three-dimensional structure, its “fourth dimension” is embodied in the metal structures of different shapes and sizes printed on the honeycomb walls, which provide extremely high flexibility for radiation regulation. This talk will systematically present the related theory, design methods, simulation strategies, and manufacturing processes. Experiments demonstrate spherical and planar lenses with wide-angle scanning capability: both designs achieve excellent beam-scanning performance and maintain nearly constant gain even at extreme scan angles. Compared with existing dielectric or PCB solutions, the paper-honeycomb solution offers very high cost-effectiveness and weight-reduction advantages. Potential applications include next-generation mobile communications requiring multi-beam wide-angle coverage, satellite communications, radar, emerging wireless access scenarios, and radio telescope systems.
Zhang Zhijun, Professor and Doctoral Supervisor, School of Automation, South China University of Technology (SCUT); Head of the Department of Intelligent Science and Technology; IEEE Fellow; AAIS Fellow; Chair of the IEEE Guangzhou Section; World’s Top 2% Scientists; National Young Talent; Director of the Guangdong Provincial Key Laboratory for Large-Model Embodied Intelligence and Humanoid Robots of Guangdong Higher Education Institutions; Deputy Chief Engineer of the Super Robot Research Institute (Huangpu); Dean of the Institute of Automation and Artificial Intelligence, Guangdong University of Petrochemical Technology.
He is the Principal Investigator of the Bionic Intelligent Robotics Laboratory, the Hongqian Intelligent Human-Computer Interaction Joint Laboratory, and the Tianxiagu Artificial Intelligence and Digital Agriculture Laboratory at South China University of Technology. He is a High-Level Talent Scholar of SCUT, recipient of the Guangdong Outstanding Youth Fund, and a Guangdong Provincial Outstanding Young Innovative Talent. His long-term research focuses on robotics, artificial intelligence, and interdisciplinary research with water conservancy.
He serves as a member of the Hybrid Intelligence Committee, the Coexisting Robotics Committee, and the Adaptive Dynamic Programming and Reinforcement Learning Committee of the Chinese Association of Automation, as well as a member of the Visual Perception and Computing Committee of the China Society of Image and Graphics. He is an editorial board member of multiple SCI journals including IEEE Transactions series, and a reviewer for more than 20 international SCI journals. He is also a review expert for the National Natural Science Foundation of China and an evaluation expert for high-tech enterprises in Guangdong Province.
He has proposed the theory of parameter-varying dynamic neural networks and robot motion optimization. He has independently developed a series of prototypes, including intelligent sediment removal robots, pipeline inspection robots, underwater 3D detectors, intelligent interactive digital humans, intelligent pineapple flower-inducing robots, power grid equipment fault diagnosis and life prediction systems, humanoid robots, mobile inspection robots, intelligent care robots, and intelligent flying robots.
Zhang Zhijun has published/accepted over 180 papers in prestigious international journals (such as Nature portfolio journals and IEEE Transactions) and conferences. He has filed more than 100 invention patents, among which over 60 have been granted. He has presided over or participated in more than 30 research projects including National Key R&D Programs, National Natural Science Foundation projects and industrial collaborative projects. A number of his patents and research achievements have obtained investment and undergone industrialization. His research has won the First Prize of Provincial Technological Invention Award, the First Prize of Provincial Science and Technology Progress Award, multiple Best Paper Awards at international conferences, and several ESI Highly Cited Papers.
This report addresses the limitations of conventional robots, including single-modality perception, poor disturbance rejection, and inadequate self-adaptability. Aligned with the requirements of Industry 4.0 and intelligent manufacturing, it focuses on breakthroughs in core embodied intelligence technologies. A theory of variable-parameter convergent dynamic learning network is proposed to tackle dynamic optimization challenges, delivering superior convergence and robustness compared with traditional approaches. A multi-modal perception learning model is constructed to acquire, process, and accurately recognize electroencephalographic, speech and visual signals. A multi-modal interaction system is established, which leverages large models to generate natural human–robot interaction behaviors and enhances the robot’s capabilities in perception, learning and decision-making. Future work will optimize perceptual data processing, advance the bionic intelligence of the decision-making layer, and promote globally optimized planning for multi-agent collaboration, so as to facilitate robot adaptation to complex scenarios.
Sun Xiaodong, male, Professor-level Senior Engineer, currently serves as the Technical Director of CISDI Information Technology (Chongqing) Co., Ltd. He was the first in the steel industry to propose and develop an integrated intelligent management and control platform for ironmaking areas based on the industrial internet. He overcame the core challenge of precise perception and stable storage/computation of 350,000 heterogeneous multi-source data points, developed 32 cross-process coupling models including blast furnace condition diagnosis and thermal control, and enabled the transformation of ironmaking operations from local optimization to holistic optimization. He established the world's first ironmaking intelligent control center, fundamentally disrupting the century-old paradigm of decentralized management in the steel industry. The achievements have been implemented at over 30 steel enterprises including Baowu, NISCO, Formosa Ha Tinh Steel (Vietnam), and JSW Steel (India), capturing a market share exceeding 80% and enabling annual CO₂ emission reductions of approximately 12 million tons at application sites. He has received honors including Chongqing Elite Program - Software Leading Talent, Distinguished Engineer of the Chinese Association of Automation, Outstanding Engineer of the China Software Industry Association, First Prize of the Chongqing Science and Technology Progress Award, and First Prize of the China Society for Metals Science and Technology Progress Award. He holds 33 authorized patents, 55 software copyrights, 25 published papers, 1 authored monograph, and has participated in the development of 6 national standards.
Industrial software is the core enabler of digital transformation in manufacturing, yet its R&D and delivery have long been plagued by systemic challenges including significant requirements misinterpretation, fragmented design-development-testing workflows, difficulty in accumulating domain knowledge, and prohibitively high customization costs. With the rapid advancement of large language models and intelligent agent technologies, AI is evolving from a mere coding-assistance tool into a collaborative agent spanning the entire development lifecycle, fundamentally reshaping the engineering paradigm of industrial software.
This report focuses on the core challenges and implementation pathways for AI to reshape the end-to-end R&D and delivery of industrial software. First, it dissects key pain points in current industrial software development such as fragmented knowledge, sequential collaboration bottlenecks, and lagging quality assurance. Second, it proposes an AI-Native end-to-end delivery architecture covering three major phases — product design, coding implementation, and testing & verification — embedding key technologies such as domain knowledge graphs, intelligent agent collaboration, and context engineering throughout the pipeline to drive a paradigm shift from "humans driving processes" to "human-AI collaboration with agent-driven automation." Third, it presents practical cases from metallurgical industrial software development, demonstrating real-world outcomes and quantified benefits. Finally, it explores the evolution of organizational structures and roles under the AI-driven delivery paradigm.