Keynote Speaker II

Prof. Jun Wang (IEEE Life Fellow, IAPR Fellow, and HKAE Fellow)
City University of Hong Kong, Hong Kong

Bio: Jun Wang is a chair professor at City University of Hong Kong. Prior to it, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and the Chinese University of Hong Kong. He also held various short-term visiting positions at USAF Armstrong Laboratory, RIKEN Brain Science Institute, Shanghai Jiao Tong University, Huazhong University of Science and Technology, and Swinburne University of Technology. He received a B.S. and an M.S. from Dalian University of Technology and a Ph.D. degree from Case Western Reserve University. He is the Editor-in-Chief of the IEEE Transactions on Artificial Intelligence and was the Editor-in-Chief of the IEEE Transactions on Cybernetics. He is an IEEE Life Fellow, IAPR Fellow, and HKAE Fellow, and a foreign member of Academia Europaea. He is a recipient of the APNNA Outstanding Achievement Award, IEEE CIS Neural Networks Pioneer Award, CAAI Wu Wenjun AI Achievement Award, and IEEE SMCS Norbert Wiener Award, among other distinctions.

Speech Title: Intelligent Information Processing via Collaborative Neurodynamic Optimization
 

Speech Abstract:  The past four decades have witnessed the emergence and growth of neurodynamic optimization, which has become a potentially powerful problem-solving tool for constrained optimization due to its inherent biological plausibility and parallel, distributed information-processing capabilities. In recent years, a hybrid intelligence framework known as collaborative neurodynamic optimization has been proposed to solve challenging optimization problems, including global, combinatorial, and mixed-integer optimization. In the framework of collaborative neurodynamic optimization, multiple recurrent neural networks are employed to perform scattered search for optimal solutions from different initial states, and a metaheuristic rule is used to reinitialize neuronal states, thereby repositioning the search toward global optimal solutions. This approach has been theoretically proved to be globally convergent and experimentally demonstrated to be effective for many applications. In this talk, I will present several specific paradigms in this framework for data processing, feature selection, supervised learning, and compressive sensing.

Keynote Speaker III

Prof. Gaurav Sharma (IEEE Fellow, SPIE fellow)
University of Rochester, USA

Bio: Gaurav Sharma is a professor in the Departments of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology, and a Distinguished Researcher in Center of Excellence in Data Science (CoE) at the Goergen Institute for Data Science at the University of Rochester. He received the PhD degree in Electrical and Computer engineering from North Carolina State University, Raleigh in 1996. From 1993 through 2003, he was with the Xerox Innovation group in Webster, NY, most recently in the position of Principal Scientist and Project Leader. His research interests include data analytics, cyber physical systems, signal and image processing, computer vision, and media security; areas in which he has 56 patents and has authored over 220 journal and conference publications. He served as the Editor-in-Chief for the IEEE Transactions on Image Processing from 2018 through 2020, and for the Journal of Electronic Imaging from 2011 through 2015. He is a member of the IEEE Publications, Products, and Services Board (PSPB) and chaired the IEEE Conference Publications Committee in 2017-18. He is the editor of the Digital Color Imaging Handbook published by CRC press in 2003. Dr. Sharma is a fellow of the IEEE, a fellow of SPIE, a fellow of the Society for Imaging Science and Technology (IS&T) and has been elected to Sigma Xi, Phi Kappa Phi, and Pi Mu Epsilon. In recognition of his research contributions, he received an IEEE Region I technical innovation award in 2008 and the IS&T Bowman award in 2021. Dr. Sharma served as a 2020-2021 Distinguished Lecturer for the IEEE Signal Processing Society.

Speech Title: Re-invigorated 2D Barcodes: Innovations and Applications

Speech Abstract:  Barcodes have long provided an effective image-based interface for communication of digital data. The traditional one-dimensional universal product (bar)code (UPC) has now been in use for over 50 years. With the proliferation of SmartPhones and tablet devices, these days the focus is increasingly on two-dimensional barcodes. Quick response (QR) codes dominate the 2D barcode application space with myriad applications, including those in advertising and promotion, financial transactions, and connectivity. With the adoption of GS1 standards, 2D barcodes are also poised to play an increasingly prominent role in product labeling, tracking, validation, and in providing richer information to consumers. In this talk, we highlight, how advancements in camera, display, and print technologies are also enabling new innovations in 2D barcodes that can enhance their data carrying capacity and functionality enabling new applications. Specifically, we present a dual-modulation approach that exploits increasing spatial resolution to increase data capacity two-fold, in-situ and without disrupting existing 2D barcode applications. We also highlight how color capture, inherently available in mobile device cameras, can be effectively used to also increase capacity, both individually and in combination with the dual modulation. We highlight several emerging applications for the proposed innovations, particularly, highlighting recent work on combating quishing, i.e., QR code based phishing attacks.

Keynote Speaker IV

Prof. Cheng-Hsuan Li
NTCU

Bio: Dr. Cheng-Hsuan Li is a Chair Professor at the Graduate Institute of Educational Information and Measurement and Director of the Center for Institutional Affairs at National Taichung University of Education, Taiwan. He served as Director-General of the Department of Information and Technology Education, Ministry of Education, Taiwan, from August 2022 to January 2024. He assisted in the planning and implementation of Taiwan’s Digital Learning Enhancement Plan for Grades 1–12 Students (2022–2025) and is currently the Principal Investigator of the Educational Big Data Project under Taiwan’s AI Talent Ark Project (2026–2029). In 2023, he led the development of TALPer, a generative AI Companion embedded in the Taiwan Adaptive Learning Platform (TALP) for nationwide K–12 use. Since its nationwide launch, TALPer and its AI Genies—such as self-directed learning, drawing, reading and thinking, and joyful learning genies—have supported over 750,000 teachers and students across Taiwan. His research focuses on artificial intelligence in education, statistical learning, educational measurement, learning analytics, adaptive learning systems, AI-supported assessment, and human-AI teaming. He advocates trustworthy and human-centered educational AI that supports personalized learning, teacher decision-making, self-regulated learning, and deeper student understanding.

Speech Title: Designing the Next Generation of AI Learning Environments: Multi-Agent Systems, Multimodal Generation, and Vibe-Coded Interactive Learning

Speech Abstract: What if an AI learning companion could identify a student's difficulty, generate an interactive mathematical model on demand, and use the resulting interaction to guide the next learning step?

This keynote explores how multi-agent systems, multimodal generation, vibe-coded interactive learning, and educational measurement can shape adaptive AI learning environments. Drawing on TALPer, an AI learning companion integrated into the Taiwan Adaptive Learning Platform (TALP), it discusses how AI can scaffold learning and generate manipulable mathematical models or scientific simulations in response to specific difficulties. Multimodal generation also supports visual, audio, and presentation materials, while persona-based multi-agent systems create collaborative scenarios involving virtual students.

Beyond content generation, the keynote examines a shift from predominantly summative assessment toward formative, process-oriented assessment. Item Response Theory (IRT) and Cognitive Diagnosis Models (CDMs) provide foundations for proficiency estimation and skill diagnosis; analyses of dialogue and interaction processes offer complementary evidence of how understanding develops. Together, these approaches suggest a feedback loop connecting diagnosis, on-demand interactive generation, learners' actions, and adaptive guidance.

The keynote concludes with a vision of human-centered AI environments that support exploration, collaboration, and deeper learning while addressing assessment validity and cognitive offloading.
 

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