Prof. Antonio Alarcón-Paredes
National Polytechnic Institute, Mexico

Bio: Dr. Antonio Alarcón-Paredes is a professor at the Intelligent Computing Laboratory of the Computer Research Center at the National Polytechnic Institute, where he also earned his Ph.D. in Computer Science. With over a decade of experience in the field, he has authored multiple articles in high-reputed journals and international conferences. He holds one granted patent and three others pending. Since 2020, he has been recognized as a Level 1 National Researcher by the Ministry of Humanities, Science, and Technology of the Mexican government and is also a member of the Mexican Society for Artificial Intelligence. His research interests include the development of algorithms and applications in areas such as image analysis, computer vision, machine learning, deep learning, intelligent computing applications, and biomedical applications.

Prof. Chinthaka Premachandra
Shibaura Institute of Technology, Japan

Bio: Chinthaka Premachandra (Senior Member, IEEE) was born in Sri Lanka. He received his B.Sc. and M.Sc. degrees from Mie University, Tsu, Japan, in 2006 and 2008, respectively, and his Ph.D. degree from Nagoya University, Nagoya, Japan, in 2011. He is a Professor in the Department of Advanced Electronic Engineering, School of Engineering and Graduate School of Engineering, Shibaura Institute of Technology, Tokyo, Japan, where he currently serves as the Director of the Image Processing and Robotics Laboratory. His research interests include AI, UAVs, image processing, audio processing, intelligent transport systems (ITS), and mobile robotics. He has authored or co-authored over 200 publications in reputed journals and conferences related to these fields.
He is currently an Associate Editor for IEEE Robotics and Automation Letters (R-AL) and IEICE Transactions on Information and Systems. He is a member of IEEE, IEICE (Japan), SICE (Japan), RSJ (Japan), and SOFT (Japan). He has received numerous awards, including the IEEE SENSORS LETTERS Best Paper Award from the IEEE Sensors Council in 2022 and the IEEE Japan Medal from the IEEE Tokyo Section in 2022.He is also the Founding Chair of the International Conference on Image Processing and Robotics (ICIPRoB), which is technically co-sponsored by IEEE.

Prof. Abril Uriarte Arcia
CIDETEC - IPN, Mexico

Bio: Dr. Uriarte is a teacher/researcher at the Center for Innovation and Development in Computing Technology (CIDETEC) of the Instituto Politécnico Nacional (IPN), México, since 2016. His areas of work include topics related to machine learning, pattern classification, neural networks, deep learning, associative memories, time series prediction, and data stream classification. She has participated in the development of projects where intelligent computing methods are applied to problems of social impact such as pre-diagnosis of diseases and environmental monitoring.
Dr. Uriarte earned a BSc in Computer Engineering from the National University of Engineering (UNI) in Managua, Nicaragua. She received her MSc and PhD in Computer Science from The Computing Research Center, IPN.
She is a professor in Artificial Intelligence Engineering and Master's in Computing Technology programs at the IPN, in Bioinspired Algorithms and Machine Learning topics. She has participated in the creation of academic programs such as the Artificial Intelligence Engineering program and the graduate program (master's and doctorate) in Artificial Intelligence Science and Technology and Data Science. Member of the IPN research networks in Computing and Artificial Intelligence and Data Science.

Assoc. Prof. Kezhi Mao
Nanyang Technological University, Singapore

Bio: Mao Kezhi obtained his BEng, MEng and PhD from Jinan University, Northeastern University, and University of Sheffield in 1989, 1992 and 1998 respectively. He is now an Associate Professor at School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. His research covers a couple of subfields of artificial intelligence (AI), including machine learning, computer vision, natural language processing, and information fusion. Over the past 25 years, he has developed novel algorithms and frameworks to address various issues in the field of artificial intelligence. He has published over hundred research papers on top journals and conferences, which have received 10000+ citations (Google Scholar).
As a strong advocate of translational research, he has collaborated with government agencies and hospitals and developed a couple of prototypes of AI systems for image processing and natural language processing. He served as consultant for a number of companies such as Deloitte & Touche, ST Engineering, Zhuyi Technologies, and Rakuten Group etc, advising on R&D of AI and machine learning.
He now serves as Member of Editorial Board of Neural Networks, Academic Editor of Computational Intelligence and Neuroscience, and General Chair, General Co-Chair, Invited Panelist, and Invited Speaker of a number of international conferences.

Asst. Prof. Sook Shin
Virginia Tech, USA

Bio: Dr. Sook Shin is a Collegiate Assistant Professor in the Department of Electrical and Computer Engineering at Virginia Tech. Her primary area of expertise is bioinformatics, particularly in the analysis of gene and disease data. Through her research, she applies advanced computational methods to better understand complex biological systems and their implications for human health. In addition to her work in bioinformatics, Dr. Sook Shin has recently expanded her research into the area of precision livestock management, using Artificial Intelligence (AI) to improve the monitoring and management of livestock. This work focuses on automated behavior analysis and predictive modeling, with the goal of optimizing livestock health and productivity. By predicting factors such as weight and body condition, her research aims to provide farmers with better tools for managing livestock efficiently while ensuring animal well-being. Dr. Sook Shin earned her Ph.D. from Virginia Tech in 2012, which laid the foundation for her current research in both bioinformatics and AI. Her interdisciplinary approach to combining computational methods with agriculture and health has opened up new possibilities for both fields, contributing valuable insights to the growing intersection of technology, biology, and farming.

Asst. Prof. Isaac Kofi Nti
University of Cincinnati, USA

Bio: Dr. Isaac Kofi Nti is an Assistant Professor and Co-lead of the Information Technology Analytics Center (ITAC) at the School of Information Technology, University of Cincinnati, Ohio, USA. He holds a Ph.D. in Computer Science from the University of Energy and Natural Resources (UENR) and brings over 16 years of experience in higher education. Dr. Nti has published over 60 research papers in highly peer-reviewed journals, garnering more than 2,400 citations worldwide. Building on his extensive experience, Dr. Nti's research interests include applied machine learning in cybersecurity, education, health informatics, energy systems, agriculture, finance, and data privacy. As a seasoned academician and researcher, Dr. Nti is dedicated to advancing the field of applied machine learning.

Assoc. Prof. Jiaxin Cai
Xiamen University of Technology, China

Bio: Jiaxin Cai received his Ph.D. degree in Information and Computation Science from Sun Yat-Sen University in 2014. He also received his M.S. degree and B.Sc. degree in Bio-medical Engineering from Southern Medical University in 2011 and 2008 respectively. Currently, he is an associate professor in the School of Mathematics and Statistics at Xiamen University of Technology. He has authored over 40 peer-reviewed papers at academic journals and conferences. His current research interests include machine learning, computer vision and bio-medical engineering.

Assoc. Prof. Weibin Wang
South China Normal University, China

Bio: Weibin Wang received his B.E. degree in 2017 from Northeastern University, China, and his M.E. in 2019 and D.E. degree in 2022 both from Ritsumeikan University, Japan. He served as an Assistant Researcher and Postdoctoral Fellow at Zhejiang Lab, Hangzhou, China, from 2022 to 2024. He is currently an Associate Research Fellow at the School of Mathematical Sciences, South China Normal University, China, and a core member of the Machine Learning and Optimization Computing Laboratory.His research interests span big data governance, Multimodal Large Language Models, AI algorithm design, and their applications in intelligent healthcare. His work focuses on advancing medical image processing, 3D medical imaging reconstruction, and mixed reality interaction for medical systems, addressing critical challenges at the intersection of medicine and artificial intelligence.
Weibin Wang published over 20 papers in international conferences and journals, including ACCV, EMBC, and others, and holds 6 awarded national invention patents. He actively contributes to the academic community as a reviewer for leading journals such as Neurocomputing and Frontiers in Oncology, as well as prestigious conferences including MICCAI and EMBC.He was honored with the Excellence Award at the 2020 Chunhui Cup Innovation & Entrepreneurship Competition for Overseas Chinese Scholars, a national event jointly organized by the Chinese Ministry of Education and Ministry of Science and Technology.

Title: "Artificial Intelligence and Mathematical Methods in Pancreatic Cancer Evolutionary Modeling and Liver Cancer Auxiliary Diagnosis"

Abstract: Recently, deep learning (DL) has become a transformative force across academic and industrial fields, particularly in medical image analysis. Despite its success in achieving cutting-edge performance, the integration of DL into clinical practice remains limited. A key challenge lies in the gap between computational models and the domain-specific anatomical knowledge inherent to medical expertise. In this presentation, we introduce novel frameworks that bridge this divide through knowledge-guided AI methodologies, focusing on two interconnected research directions:
(1) Evolutionary Modeling of Pancreatic Cancer: We propose a hybrid computational framework that synergizes mathematical modeling with Physics-Informed Neural Networks (PINNs). By integrating phase-field equations—a physics-based approach for simulating tumor growth dynamics—with PINNs, our method captures the spatiotemporal evolution of pancreatic cancer. This approach combines experimental data with tumor growth modeling simulations, enabling dynamic predictions of tumor progression.
(2) AI-Agent Systems for Liver Cancer Diagnosis: We develop an AI-Agent architecture that combines multimodal large language models (LLMs) with structured medical knowledge. Leveraging clinical guidelines and clinician expertise, the system processes imaging and clinical text. A key innovation is the incorporation of diagnostic guidelines and expert consensus as semantic constraints, ensuring strict adherence to clinical protocols while maintaining decision stability.

Dr. Vishnu S. Pendyala
San Jose State University, USA

Bio: Vishnu S. Pendyala, PhD is a faculty member in Applied Data Science and an Academic Senator with San Jose State University, current chair of the IEEE Computer Society Santa Clara Valley Chapter, and IEEE Computer Society Distinguished Contributor. During his recent 3-year term as an ACM Distinguished Speaker and before that as a researcher and industry expert, he gave numerous (80+) talks in various reputed forums. Some of these talks are available on YouTube and IEEE.tv. He is a senior member of the IEEE and ACM and has over two decades of experience in the software industry in the Silicon Valley, USA. His book, “Veracity of Big Data,” is available in several libraries, including those of MIT, Stanford, CMU, the US Congress and internationally. In 2023, Dr. Pendyala served on the US government's National Science Foundation (NSF) proposal review panel.

Dr. Rahul Kumar JAIN
Tiwaki Co., Ltd. Japan

Bio: Rahul Kumar JAIN received BCA degree from Dr. H. S. Gour Central University, Sagar, India, and MCA degree from RGPV University, Bhopal, India in 2011 and 2015, respectively. He received PhD degree from Ritsumeikan University, Japan in 2022. He was a starting assistant professor and a researcher fellow at Ritsumeikan University, Japan. He is currently an AI researcher at Tiwaki Co., Ltd. Japan. His current research interests include computer vision, machine learning, and image processing.

Title: "Multimodal Learning and Domain Adaptation for Generalized Detection in Medical and Real-World Images"

Abstract: Accurate and generalizable detection in medical imaging is essential for developing reliable computer-aided diagnosis (CAD) systems, particularly in complex tasks such as liver lesion detection using various medical imaging modalities. In this talk, two complementary research directions are introduced to address key challenges: domain adaptation and multimodal learning for detection tasks across diverse medical imaging scenarios. First, domain adaptation techniques are presented to address the problem of domain shift, which arises due to variations in imaging protocols, scanner types, and multi-center data distributions. A recognition framework is employed that incorporates adversarial learning and divergence-based feature alignment to minimize domain gaps across CT phases and acquisition centers. In addition, a multimodal detection approach is discussed, in which image and text features are jointly utilized to enhance anomaly recognition across different medical imaging modalities. The framework enriches semantic understanding through textual attributes, such as category, location, and size, extracted from existing annotations, without requiring additional labelling. During inference, the model operates in a text-free mode, ensuring efficiency and eliminating reliance on text prompts. These approaches collectively demonstrate how domain adaptation and multimodal learning strategies can be applied to develop generalizable and efficient detection systems in medical imaging. Experimental results across multiple datasets are presented to validate the effectiveness of the proposed methods in addressing challenges in both medical and real-world imaging scenarios.