Keynote Speakers

Prof. Yen-Wei Chen
Ritsumeikan University

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. 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.

Speech Title: "From Data-Centric to Knowledge-Guided: Advancing Bio-Medical Image Analysis with Multimodal Deep Learning"

Abstract: Deep Learning (DL) has achieved remarkable success across various domains, particularly in computer vision and image recognition. In medical imaging, although DL models have demonstrated state-of-the-art performance, their translation into real-world clinical practice remains limited. This gap stems from two fundamental challenges: the scarcity of large-scale annotated datasets and the "black-box" nature of conventional DL models, which undermines clinical trust and interpretability. In this talk, I will present a paradigm shift from data-centric deep learning toward knowledge-guided multimodal learning—an approach that integrates clinical wisdom as an active component of the model itself, ad introduce two complementary strategies for infusing domain knowledge into deep learning:

(1) Explicit knowledge guidance, which integrates unstructured radiology reports with medical images using cross-modal attention mechanisms to enhance both accuracy and explainability.
(2) Implicit knowledge distillation, where genetic information serves as a teacher during training, transferring its predictive power to a student network that relies solely on CT images at inference time—enabling scalable, cost-effective precision diagnostics.

Prof. Xiaobing Yan
Hebei University

Prof. Xiaobing Yan is a Changjiang Scholar of the Ministry of Education in advanced semiconductor materials and intelligent chips, a Distinguished Professor under a national major talent program, and the chief scientist of a National Key R&D Program. He is currently Dean of the Graduate School of Hebei University and serves part-time as Deputy Secretary of the Hebei Provincial Youth League Committee. Prof. Yan has long focused on memristors, hafnia-based ferroelectric materials, brain-inspired intelligent chips, and algorithm optimization. His group has developed a systematic research framework linking materials and interface engineering, device physics, array and chip integration, and intelligent application demonstrations. He has led more than ten national research projects, including projects funded by the National Key R&D Program and the National Natural Science Foundation of China. His work has appeared in Nature Electronics, Nature Communications, Science Advances, Physical Review Letters, Advanced Materials and other leading journals. He has received the top prize in the National Disruptive Technology Innovation Competition, the First Prize of Natural Science of Hebei Province, and the Second Prize of Natural Science of the Ministry of Education.

Speech Title: "Epitaxial Growth and Physical Mechanisms of Hafnia-Based Ferroelectrics"

Abstract: Hafnia-based ferroelectric materials combine excellent thickness scalability with CMOS compatibility and are promising platforms for next-generation nonvolatile memories, ferroelectric transistors, and intelligent information devices. However, the ferroelectric orthorhombic phase is thermodynamically metastable, and the mechanisms governing phase selection, interface reconstruction, charge transfer, and high-polarization formation in epitaxial films remain central challenges. This keynote will discuss controllable epitaxial growth of hafnia-based ferroelectric thin films and the underlying physical mechanisms. The talk will first introduce how bottom-electrode termination engineering in La0.67Sr0.33MnO3 can stabilize the ferroelectric orthorhombic phase in Hf0.5Zr0.5O2 and preserve clear ferroelectric orientation down to 1.5 nm. It will then examine composition-tuned La1-xSrxMnO3 buffer layers, where interfacial charge transfer and hole doping can be continuously regulated, revealing that moderate hole doping promotes orthorhombic phase stability whereas excessive doping suppresses ferroelectric phase formation. Finally, the talk will present epitaxial HfZrO2/HfLaO2 multilayer structures, in which interlayer strain coupling and local structural distortion produce intrinsic polarization approaching the theoretical limit. These studies establish a coherent picture from epitaxial growth and interface engineering to polarization enhancement, offering new design principles for high-performance hafnia-based ferroelectric devices and future intelligent chips.

 

Prof. Lim Chee Peng
Swinburne University of Technology, Australia

LIM Chee Peng received his Ph.D. degree from the University of Sheffield, UK, in 1997. His research focuses on Computational Intelligence-based models and their applications. Wiith over 650 technical papers, he collaborates widely with researchers in the international arena with the support of numerous prestigious international fellowships. These include the Australia-India Senior Visiting Fellowship by Australian Academy of Science, Australia-Japan Emerging Research Leaders Exchange Program by Australian Academy of Technology and Engineering, UK Commonwealth Fellowship at University of Cambridge, US Fulbright Fellowship at University of California, Berkeley as well as Visiting Scientists Programs at both Harvard University and Stanford University.

Speech Title: "Computational Intelligence for Healthcare: From Advanced Algorithms to Responsible Deployment"

Abstract: Computational intelligence (CI) offers a powerful paradigm for pattern recognition and data-driven decision-making. Inspired by the principles of human and biological intelligence, it encompasses a diverse range of methodologies, including neural networks and deep learning, fuzzy systems, evolutionary computation, and hybrid intelligent approaches. This talk explores key CI algorithms and their real-world applications in the healthcare sector. The presentation explains how CI models can be leveraged to tackle persistent challenges such as data robustness, interpretability, and privacy. Beyond advancing personalised medicine through patient-specific treatment predictions, this talk also discusses responsible deployment strategies to ensure CI solutions remain safe and trustworthy for tomorrow's healthcare.

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