Hong Kong University of Science and Technology, Hongkong, China

James Kwok is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He is an IEEE Fellow.

Prof Kwok received his B.Sc. degree in Electrical and Electronic Engineering from the University of Hong Kong and his Ph.D. degree in computer science from the Hong Kong University of Science and Technology. He then joined the Department of Computer Science, Hong Kong Baptist University as an Assistant Professor. He returned to the Hong Kong University of Science and Technology and is now a Professor in the Department of Computer Science and Engineering. He is serving / served as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, Neural Networks, Neurocomputing, Artificial Intelligence Journal, International Journal of Data Science and Analytics, and on the Editorial Board of Machine Learning. He is also serving as Senior Area Chairs of major machine learning / AI conferences including NeurIPS, ICML, ICLR, IJCAI, and as Area Chairs of conferences including AAAI and ECML. He is on the IJCAI Board of Trustees. 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". Prof Kwok is the IJCAI-2025 Program Chair.

University of Tsukuba, Japan

He received the B.Sc. degree from the Department of Electrical Communications, Tohoku University, Japan, in1985, and the Ph.D. degree from the Graduate School of Engineering, Tohoku University, in 1990. In 1992, he joined the University of Tsukuba, Japan. He is currently a Professor with the Institute of Systems and Information Engineering, University of Tsukuba, Japan. His research areas include medical imaging, image processing, and inverse problems. In particular, he is actively working on tomographic image reconstruction for X-ray CT, PET, SPECT, and electron tomography. He received best paper awards more than 10 times from various international and Japanese societies. He received the IEICE (The Institute of Electronics, Information, and Communication Engineers, Japan) Fellow award for his contributions on “cross-sectional image reconstruction methods in medical computed tomography”. In 2018, he obtained Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology for his contributions on “research on design method and image reconstruction method for new CT”. For 2011-2016, he was an Editor-in-Chief of the Journal of Medical Imaging Technology (MIT). From 2020, he is a president of Japanese Society of Medical Imaging Technology (JAMIT).

Prof. Kenji Suzuki
Institute of Science Tokyo, Japan

Kenji Suzuki, Ph.D. (Nagoya University) worked at Hitachi Medical Corp, Aichi Prefectural University, Japan, as a faculty member, in Department of Radiology, University of Chicago, as Assistant Professor, and Medical Imaging Research Center, Illinois Institute of Technology, as Associate Professor (Tenured). He is currently a Professor (Tenured) & Director of Biomedical Artificial Intelligence Research Unit in Institute of Innovative Research, Tokyo Institute of Technology, Japan. He published more than 395 papers (including 125 peer-reviewed journal papers). He has been actively researching on deep learning in medical imaging and AI-aided diagnosis in the past 25 years, especially his early deep-learning model was proposed in 1994. His papers were cited more than 16,000 times, and his h-index is 63. He is inventor on 38 patents (including ones of earliest deep-learning patents), which were licensed to several companies and commercialized. He published 15 books and edited 16 journal special issues. He has been awarded numerous grants including NIH, NEDO, and JST grants, totaling $17M. He serves as Editors of 34 leading international journals including Pattern Recognition. He chaired 114 international conferences. He received 27 awards, including 3 Best Paper Awards in leading journals. His research interests include Artificial intelligence, Deep learning, Machine learning, AI-aided system, Computer-aid diagnosis, Medical image analysis, Medical image processing, Data science.