Speakers

Speech Title : "Explainable and Robust Deep Learning for Medical Image Diagnosis: From Theory to Clinical Impact"

Prof. Madya Dr. Razali bin Yaakob is an Associate Professor at the Faculty of Computer Science and Information Technology. His research and supervision expertise span Computable Structures and Scientific Computing, Computer and Embedded Systems Engineering, Data Science, and Intelligent Systems. His scholarly work focuses on meta-heuristic optimization, artificial intelligence, deep learning, data mining, and intelligent decision-support systems, with notable contributions to educational timetabling, medical image analysis (particularly tuberculosis detection), and many-objective optimization. He has published extensively in reputable international journals and conferences, reflecting strong interdisciplinary research impact across AI, optimization, and real-world applications.

Speech Title : Enhancing Trust in AI-Driven Decision-Making: Explainable AI for Smart Governance

Dr. Shakila Basheer is an Associate Professor in the Information Systems Department at Princess Nourah bint Abdulrahman University, Saudi Arabia. Her research focuses on artificial intelligence, explainable AI (XAI), data engineering, and digital governance. She has been recognized among the world’s top 2% scientists (Stanford list) and is a Fellow of Advance HE (FHEA), UK. She actively contributes to research, teaching in data science and AI applications.

Speech Title : Fast queries of Delaunay triangulation-based classification

Gene Eu Jan received the B.S. degree in electrical engineering from National Taiwan University, Taipei, Taiwan, in 1982 and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Maryland, College Park, MD, USA, in 1988 and 1992, respectively. He has been a Professor with the Departments of Computer Science and Electrical Engineering, National Taipei University, New Taipei City, Taiwan, since 2004, where he was also the Dean of the College of Electrical Engineering and Computer Science from 2007 to 2009. In addition, professor Jan servers as the president of Tainan National University of the Arts from August 2016 to July 2024. Now he serves as a chair professor with the Departments of Computer Science, Asia University, Taiwan, since 2024, He has published more than 270 articles related to data science, parallel computer systems, interconnection networks, motion planning, electronic design automation, and VLSI systems design in journals, conference proceedings, books and patents.

Speech Title : The Next Generation of Cyber Attacks with Generative AI

Dr. Almuthanna Alageel is a cybersecurity researcher, entrepreneur, and academic specializing in artificial intelligence for advanced threat detection and automated defense. He serves as Director of the Cyber Intelligence Lab at KACST’s Institute of Cybersecurity and is an Honorary Research Associate at Imperial College London, where he earned his PhD in AI and Cybersecurity. He is also an adjunct assistant professor at Prince Sultan University and Alfaisal University, teaching AI for Cybersecurity and advanced security topics. Dr. Alageel is the Co-Founder and Chief Scientist of APTWatch, a startup focused on AI-powered threat intelligence, SOC automation, and next-generation intrusion detection systems. His work bridges research and commercialization, with projects spanning NIDS, HIDS, vulnerability detection, and LLM-based cyber defense systems. His research interests include adversarial machine learning, large language models for cybersecurity, intelligent SIEM systems, and real-time APT detection for critical infrastructure and smart cities. He holds professional certifications including CISSP, CISM, PMP, and CRISC, and actively collaborates with international research institutions and industry partners to advance applied AI in cybersecurity.

Speech Title : Advancing Micro-Expression Recognition with SepViT: A Dual-Path Transformer-Convolution Approach

Muhammad Zaman is a University Lecturer and AI researcher specializing in Machine Learning, Deep Learning, and Computer Vision, with a focus on explainable and data-efficient intelligent systems for medical image analysis and affective computing. His research contributions include transformer-based architectures, attention mechanisms, and hybrid deep learning frameworks for healthcare diagnostics and micro-expression recognition. He has published extensively in leading international journals and conferences, including IEEE Access, IEEE Transactions, Artificial Intelligence Review, and ACM/IEEE BDCAT, with multiple Q1-indexed publications as corresponding author. His recent work includes the development of advanced transformer-convolution frameworks such as SepViT for micro-expression recognition. Zaman has supervised more than 25 MS theses and 75 undergraduate research projects, contributing to research-driven teaching and interdisciplinary collaboration. He is also an active member of IEEE and its professional societies, promoting impactful AI research and innovation. His current research focuses on scalable, interpretable, and real-world AI solutions for healthcare and intelligent computing systems.