 | Prof.Dr. J. Joshua ThomasUniversity of Wollongong (UOW) Dr J. Joshua Thomas is a Professor of Computer Science at the University of Wollongong Malaysia, where he leads research and academic initiatives in modern AI and data analytics. He holds a PhD in Intelligent Systems Techniques from Universiti Sains Malaysia, with his doctoral research focusing on adaptive computational frameworks for complex decision-making systems. With more than two decades of experience in higher education and research, Prof Thomas’s expertise spans intelligent systems, deep learning, graph neural networks, quantum machine learning, and data-driven optimization. His current research explores the integration of deep learning and graph-based models for applications such as drug discovery, autonomous systems, and intelligent process automation. Prof Thomas has authored and edited over a dozen books published by leading academic publishers including Wiley, Elsevier, and IGI Global, and has contributed more than fifty scholarly papers to high-impact journals and international conferences. He also serves as an editorial board member and guest editor for several international journals in computing and engineering, including the International Journal of Energy Optimization and Engineering. Beyond his scholarly work, Prof Thomas has served in multiple leadership roles, including Head and Deputy Head of the Department of Computing, and has been actively involved in collaborative research projects supported by national and international grants. He is regularly invited to deliver keynote and plenary talks, workshops, and lectures at international conferences and universities, sharing insights on emerging trends in artificial intelligence, data analytics, and computational intelligence. Prof Dr J. Joshua Thomas continues to advance research that bridges theoretical innovation with practical solutions, contributing significantly to the evolving landscape of intelligent computing and data-driven decision systems. Title: Integrating Graph Neural Networks for Accurate Prediction of Drug-Target Interactions.
Abstract： Accurate prediction of drug-target interactions (DTIs) is essential for expediting the drug discovery process. Although traditional deep learning models such as convolutional and recurrent neural networks have been widely used in this domain, they often struggle to leverage the intrinsic graph-based structure of molecular data. To address this challenge, a framework that incorporates three advanced graph neural network (GNN) architectures, GENConv, GCNConv, and HypergraphConv to effectively learn the structural and chemical properties of drug compounds. The resulting drug and target representations are fused and processed through fully connected layers to predict binding affinity values. This framework was evaluated using two established benchmark datasets and a newly introduced dataset (Allergy), consistently outperforming traditional baseline models and demonstrating strong potential for accurate and robust DTI prediction. |
 | Prof. Ag. Asri Ag. IbrahimUniversiti Malaysia Sabah (UMS) Ag. Asri Ag. Ibrahim is a Professor at the Faculty of Computing and Informatics, Universiti Malaysia Sabah (UMS), and has served as the university's Chief Digital Officer (CDO) since 2020. With a dedicated career at UMS spanning over 26 years, he brings a wealth of academic and administrative experience to his roles. He earned his Doctor of Philosophy (PhD) in Electronics from The University of York, United Kingdom, in 2008. Prior to his doctoral studies, he completed both his Master’s Degree in Computer Science and his Bachelor’s Degree in Computer Science at Universiti Malaya. He is an active and prolific researcher. His primary research interests include data sonification, emotional (Kansei) engineering, artificial intelligence and human-computer interaction. He has authored and co-authored over 144 articles and proceedings, achieving an H-Index of 20. He has also served as a principal investigator and research member on more than 37 research grants. His research findings have been widely disseminated at national and international seminars, where he has been featured as both an invited and keynote speaker. Furthermore, his work has garnered recognition at innovation competitions, winning medals at both national and international levels.Beyond his academic contributions, he is actively involved in community engagement, including STEM programs and technology empowerment initiatives for entrepreneurs. In recognition of his expertise, he has been appointed as a subject matter expert and a member of the digital entrepreneurship committee for the Malaysia Digital Economy Corporation (MDEC). Title: Toward Empathic BCIs: Fusing EEG, Emotion, and Generative AI in Smart Healthcare
Abstract： As brain–computer interfaces (BCIs) evolve beyond control and communication, a new frontier emerges which is empathic intelligence. This keynote explores how the fusion of EEG-based emotion decoding, sonification, and generative AI can transform brain data into meaningful, human-centered interactions. By integrating affective computing with neural signal processing, empathic BCIs enable machines not only to interpret mental states but to respond through adaptive sensory feedback such as sound or visuals. The talk presents recent developments in emotion-aware neuro-wearables, multimodal signal fusion, and generative feedback models, highlighting applications in mental health monitoring, neuro-rehabilitation, and personalized therapy. Ultimately, it envisions a future of smart healthcare where technology becomes emotionally attuned that bridging the gap between neural signals, affective understanding, and compassionate care. |
 | Associate Professor Ts. Dr Aslina BaharumSunway University Ts. Dr Aslina Baharum is an Associate Professor and UX Researcher at the School of Engineering and Technology, Sunway University. Previously, she was a Senior Lecturer at Universiti Teknologi MARA (UiTM), and Universiti Malaysia Sabah (UMS). She also has industry experiences where she worked as an IT Officer for the Forest Research Institute of Malaysia (FRIM). She had experienced more than 20 years in the IT field. She received a PhD in Visual Informatics (UKM), a Master Science degree in IT (UiTM) and graduated Bachelor of Science (Hons.) in E-Commerce from UMS. She is a member of the Young Scientists Network - Academy of Science Malaysia, Senior Member IEEE, and certified Professional Technologist from MBOT, and served as MBOT/MQA auditor. She won several medals in research and innovation showcases and was awarded several publication awards, teaching awards, Excellence Service award, and UMS Researchers Awards. She has co-authored and editor books, published several books of chapters (>20), technical papers in conferences and peer-reviewed and indexed journals (>60) papers. She also served as editor for several journals, scholarly contributed as a committee, editorial team and reviewers, and given several invited/ plenary talks at conferences. Her research interests include UX/UI, HCI/Interaction Design, Product & Service Design, Software Engineering & Mobile Development, Information Visualization & Analytics, Multimedia, ICT, IS and Entre/Technopreneurship. Her workshops and talks covered Entrepreneurship, Video/Image Editing, E-Commerce/Digital Marketing, AR/VR/MR/XR in STEM, Design Thinking and etc. She is also a Certified Professional Entrepreneurial Educator, Executive Entrepreneurial Leaders and HRDF Professional Trainer.
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 | Prof. Lisheng XuNortheastern University Lisheng Xu (Senior Member, IEEE) received the B.S. degree in electrical power system automation, the M.S. degree in mechanical electronics, and the Ph.D. degree in computer science and technology from the Harbin Institute of Technology, Harbin, China, in 1998, 2000, and 2006, respectively. He is currently a Full Professor with the College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, China. He has authored or coauthored 139 international research papers, and holds fifteen invention patents. His research interests include nonlinear medical signal processing, computational electromagnetic simulation, medical imaging, and pattern recognition. He is the Director of theory and education professional committee of China Medical Informatics Association. |