Prof. James Kwok
IEEE Fellow, Hong Kong University of Science and Technology, Hongkong
 

Speech title: Unlock Your Potential: Achieving Multiple Goals with Ease

Abstract: Multi-objective optimization (MOO) aims to optimize multiple conflicting objectives simultaneously and is becoming increasingly important in deep learning. However, traditional MOO methods face significant challenges due to the non-convexity and high dimensionality of modern deep neural networks, making effective MOO in deep learning a complex endeavor.

In this talk, we address these challenges in MOO for several deep learning applications. First, in multi-task learning, we propose an efficient approach that learns the Pareto manifold by integrating a main network with several low-rank matrices. This method significantly reduces the number of parameters and helps extract shared features. We also introduce preference-aware model merging, which uses MOO to combine multiple models into a single one, treating the performance of the merged model on each base model's task as an objective. During the merging process, our parameter-efficient structure generates a Pareto set of merged models, each representing a Pareto-optimal solution tailored to specific preferences. Finally, we demonstrate that pruning large language models (LLMs) can be framed as a MOO problem, allowing for the efficient generation of a Pareto set of pruned models that illustrate various capability trade-offs.

Biography: 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 Action Editor of Machine Learning. He is also serving as Senior Area Chairs of major machine learning / AI conferences including NeurIPS, ICML and ICLR. 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 the IJCAI-2025 Program Chair.

 

Prof. Yen-Wei Chen
Ritsumeikan University, Japan
 

Speech title: Towards Accurate AI-Based Segmentation of Biomedical Images

Abstract: Recently, Deep Learning (DL) has played an important role in various academic and industrial domains, especially in computer vision and image recognition. Although deep learning (DL) has been successfully applied to bio-medical image analysis, achieving state-of-the-art performance, few DL applications have been successfully implemented in real clinical settings. The primary reason for this is that the specific knowledge and prior information of human anatomy possessed by doctors is not utilized or incorporated into DL applications. In this keynote address, I will present our recent advancements in knowledge-guided deep learning for enhanced bio-medical image analysis. This will include two research topics: (1) our proposed deep atlas prior, which incorporates bio-medical knowledge into DL models; (2) language-guided bio-medical image segmentation, which incorporates the specific knowledge of doctors as an additional language modality into DL models.

Biography: Yen-Wei Chen received the B.E. degree in 1985 from Kobe Univ., Kobe, Japan, the M.E. degree in 1987, and the D.E. degree in 1990, both from Osaka Univ., Osaka, Japan. He was a research fellow with the Institute for Laser Technology, Osaka, from 1991 to 1994. From Oct. 1994 to Mar. 2004, he was an associate Professor and a professor with the Department of Electrical and Electronic Engineering, Univ. of the Ryukyus, Okinawa, Japan. He is currently a professor with the college of Information Science and Engineering, Ritsumeikan University, Japan. He is the founder and the first director of Center of Advanced ICT for Medicine and Healthcare, Ritsumeikan University, Japan. Since April 2024, he has been a Foreign Fellow of the Engineering Academy of Japan.
His research interests include medical image analysis, computer vision and computational intelligence. He has published more than 300 research papers in a number of leading journals and leading conferences including IEEE Trans. Image Processing, IEEE Trans. Medical Imaging, CVPR, ICCV, MICCAI. He has received many distinguished awards including ICPR2012 Best Scientific Paper Award, 2014 JAMIT Best Paper Award. He is/was a leader of numerous national and industrial research projects. In recent years, Professor Yen-Wei Chen has consistently been ranked among the world’s top 2% of scientists, both for the most recent year and over his entire career, according to the Stanford/Elsevier rankings.

 

Prof. Ari Aharari
Sojo University, Kumamoto, Japan
 

Speech title: Harmonizing Nature, Industry, and Safety: AI and IoT Approaches toward a Resilient and Sustainable Society

Abstract: Realizing a Sustainable Society (SS) requires a holistic approach that integrates environmental conservation, industrial efficiency, and social resilience. As Artificial Intelligence (AI) and IoT technologies evolve, their ability to bridge the physical and digital worlds becomes crucial for solving complex global challenges. In this keynote speech, I will discuss how AI-driven technologies can contribute to the Sustainable Development Goals (SDGs) through three distinct yet interconnected case studies: environmental rehabilitation, smart manufacturing, and disaster mitigation.

The first part of the talk focuses on "IoT-Based Monitoring in Mangrove Ecosystems," a collaborative project between my laboratory and our partner university in Phuket, Thailand. Mangroves are vital for marine biodiversity, coastal protection, and carbon sequestration but face rapid decline. Successful rehabilitation relies heavily on sitespecific knowledge, particularly hydrology, as mangroves are sensitive to tidal shifts, salinity, temperature, and storm resilience. We developed a mangrove-specific IoT framework and sensor prototype, verified via field testing in Phuket. This system collects onsite environmental data and transmits it to a cloud server, allowing stakeholders to assess conditions against mangrove health standards and make informed, timely decisions for the survival of young mangroves.

The second part introduces the latest initiatives at the Smart Society Innovation Laboratory, focusing on social and industrial implementation. I will present our work on Smart Factories, specifically AI-based quality control and IoT platform design, which aims to minimize waste and optimize energy consumption in manufacturing. Furthermore, I will discuss Disaster Prevention and Mitigation, introducing "Vehiclebased evacuee support systems" initiative. These projects demonstrate how AI can enhance safety and resilience in the face of natural disasters.

Through these diverse examples, this presentation aims to clarify the role of AI technologies not just as tools for efficiency, but as essential infrastructure for a truly sustainable and resilient society.

Biography: He received M.E. and PhD in Industrial Science and Technology Engineering and Robotics from Niigata University and Kyushu Institute of Technology, Japan in 2004 and 2007, respectively. In 2004, he joined GMD-JAPAN as a Research Assistant. He was Research Scientist and Coordinator at FAIS- Robotics Development Support Office from 2004 to 2007. He was a Postdoctoral Research Fellow of the Japan Society for the Promotion of Science (JSPS) at Waseda University, Japan from 2007 to 2008. He served as a Senior Researcher of Fukuoka IST involved in the Japan Cluster Project from 2008 to 2010. In 2010, he became an Assistant Professor at the faculty of Informatics of Nagasaki Institute of Applied Science. Since 2012, he has been Associate Professor at the department of Computer and Information Science, Sojo University, Japan. He is currently professor at the department of Computer and Information Science, Sojo University, Japan.
His research interests are IoT, Robotics, IT Agriculture, Image Processing and Data Analysis (Big Data) and their applications. He is a member of IEEE (Robotics and Automation Society), RSJ (Robotics Society of Japan), IEICE (Institute of Electronics, Information and Communication Engineers), IIEEJ (Institute of Image Electronics Engineers of Japan).