Yong Luo Wuhan University, China | BIO: Yong Luo received the B.E. degree in Computer Science from the Northwestern Polytechnical University, Xi’an, China, and the D.Sc. degree in the School of Electronics Engineering and Computer Science, Peking University, Beijing, China. He was a Research Fellow with the School of Computer Science and Engineering, Nanyang Technological University, and is currently a Professor with the School of Computer Science, Wuhan University, China. His research interests are primarily on machine learning and data mining with applications to visual information understanding and analysis. He has authored or co-authored over 100 papers in top journals and prestigious conferences including Nature Machine Intelligence, Nature Communications, IEEE T-PAMI and IJCV. He is serving on editorial board for IEEE T-MM. He received the IEEE Globecom 2016 Best Paper Award, and was nominated as the IJCAI 2017 Distinguished Best Paper Award. He is also a co-recipient of the IEEE TMM 2023, IEEE ICME 2019 and IEEE VCIP 2019 Best Paper Awards. Speech Title：Deep Model Fusion Abstract：The paradigm of deep learning has significantly evolved in recent years, moving beyond traditional supervised learning to incorporate knowledge transfer and model editing. While these emerging techniques show promise in enhancing performance, accelerated training, and reducing labeled data dependency, their full potential and scalability to large foundation models remain unexplored. This talk provides an investigation of knowledge transfer and model fusion techniques for deep neural networks, covering (1) their background, motivation, and existing approaches; (2) a taxonomy for categorizing these techniques and the formal definitions for each category; (3) our recent contributions to the field, including adaptive model ensemble, plug-and-play techniques for improved model merging, adaptive weight-based model mixing, and model merging in Pareto optimization contexts; (4) discussion of the strengths, challenges, and future directions of model fusion techniques, providing a comprehensive overview of this rapidly evolving area in deep learning. |
Prof. Tian Wang Beijing Normal University, China | BIO: Prof. Yu Xinguo is Professor at the National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China. He also holds an adjunct professorship at the University of Wollongong, Australia. He is the Chair of the Hubei Society of Artificial Intelligence in Research and Education. Prof. Yu's research primarily focuses on HI-AI collaboration, intelligent education, intelligent research, educational robotics, multimedia analysis, computer vision, and machine learning. With over 200 published research papers including over 30 SCI papers. Prof. Yu serves as an Associate Editor and Guest Editor for several international journals and has contributed significantly to the global academic community by serving as General Chair, Keynote Speaker, and Program Chair for more than 30 international conferences. Since 2021, he has pioneered and led the annual International Conference on Intelligent Education and Intelligent Research. Speech Title：AI and the Transformation of Work Paradigms Abstract：Artificial Intelligence (AI), particularly large language models (LLMs), is reshaping work paradigms across a wide range of domains that involve human intelligence. First, AI has transformed traditional research into a collaborative effort between humans and machines. Remarkably, AI-assisted research has even contributed to Nobel Prize-winning work. Second, AI technologies are redefining education by acting as alternative forms of instruction, influencing not only how students learn but also how they think and interact. Third, AI is revolutionizing work in numerous other sectors, including public service and home-based roles, by introducing new modes of operation and enhancing productivity. This talk explores these paradigm shifts and examines the implications of AI's pervasive role in the modern workforce. |
Liang Chen The University of Northern British Columbia (UNBC), Canada | BIO: Dr. Liang Chen is a Professor of Computer Science at the University of Northern British Columbia (UNBC) in British Columbia, Canada, where he has served since 2001 and led the department as chair (2005–2009) and acting chair (2021–2022). He earned a BSc in Computer Software from Huazhong University of Science and Technology (1988) and a PhD in Computer Science from the Institute of Software, Chinese Academy of Sciences (1994). Before joining UNBC, he worked in academia and industry in China, Japan, and France. His research spans pattern recognition, image processing, computational geometry, intelligent language tutoring systems, data mining, bioinformatics, computational intelligence (including fuzzy systems and neural networks), and voting schemes. He was the founding chair of the IEEE Northern British Columbia Subsection, and his biography appears in the 20th and 21st editions of Marquis Who’s Who in the World. Speech Title: Seeing Like a Machine: Why Stability Beats Accuracy in Subjective Recognition Abstract: In domains such as human or animal face recognition—and even leadership selection—clear, universally accepted standards for “correct” outcomes are often absent. These are subjective pattern-recognition problems. Computing machines excel at tasks requiring massive computation, yet when rules are underspecified, performance targets become misaligned with problem knowledge. This talk examines the expectation–knowledge gap and argues that pursuing high accuracy on arbitrary or shifting labels reduces research to trial and error. Instead, evaluation should prioritize stability—the consistency of outputs under perturbations to data, labels, model settings, and noise. Centering stability provides a more meaningful basis for progress on subjective tasks and better aligns algorithm design with real-world uncertainty. |
