Professor Qi Zhao

University of Science and Technology Liaoning, China

Zhao Qi, born in 1982 in Anshan, Liaoning Province, is a male Professor and Doctoral Supervisor. He holds a Ph.D. in Science and completed his postdoctoral training at Xiamen University. He earned his Bachelor of Science degree in 2005 from the School of Mathematics and Statistics at Wuhan University. From 2009 to 2010, he was a Joint-Training Ph.D. student in the Department of Mathematics at Michigan State University, USA. In 2011, he obtained his Ph.D. in Science from the Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences.

He was selected for the "Ten-Thousand Talent Level" in the Seventh Batch of Liaoning Province's "Hundreds, Thousands, and Ten-Thousands of Talents Project" in 2013, recognized as a Top Talent in Shenyang in 2018, and named a Highly Cited Researcher by Clarivate in 2024. He has been a visiting scholar at Tel Aviv University (Israel), the International Centre for Theoretical Physics (Italy), and Systems Biology Ireland at University College Dublin (Ireland). Additionally, he holds the positions of Guest Professor at the Wenzhou Institute, University of Chinese Academy of Sciences, and Technical Consultant at the Yangtze Delta Region Institute of Tsinghua University, Zhejiang

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Professor Liang Hu

TongJi University, China

Dr. Qi Zhao received his Bachelor degree and PhD from Wuhan University and Chinese Academy of Sciences in 2005 and 2011, respectively. Since 2011, he has been a faculty member as a lecturer and associate professor in Liaoning University, China. Now he is a full professor in University of Science and Technology Liaoning, China. Dr. Qi Zhao has been associate editor or editorial board member for more than 10 SCI journals, such as Journal of Cellular and Molecular Medicine, Pattern Recognition, Journal of Translational Medicine, IEEE Transactions on Computational Biology and Bioinformatics and so on. During recent years, he has published more than 100 SCI journal articles in the fields of bioinformatics, deep learning, computational toxicology and medical artificial intelligence. Dr. Qi Zhao received highly cited researcher awards by Clarivate Analytics in 2024. 

Title: Prediction of Molecular Properties and FGFR1 Inhibitors Based on Multi-task Self-supervised Learning

Abstract:Studying the molecular properties of drugs and their interactions with human targets helps to better understand the clinical performance of drugs and guides drug development. In computer-aided drug discovery, it is crucial to utilize efficient molecular feature representations for predicting molecular properties and designing ligands with high binding affinity to targets. Furthermore, the predictive performance and computational efficiency of the model also face challenges. In order to address the aforementioned limitations, we propose a multi-task self-supervised deep learning framework (MTSSMol), which utilizes approximately 10 million unlabeled molecular data for pre-training to identify potential inhibitors of fibroblast growth factor receptor 1 (FGFR1). During MTSSMol’s pre-training, we employ a graph neural network (GNN) encoder to learn molecular representations and propose a multi-task self-supervised pre-training strategy to fully capture the structural and chemical information of molecules. Furthermore, we validate MTSSMol’s ability to identify potential FGFR1 inhibitors through RoseTTAFold All-Atom (RFAA) molecular docking and molecular dynamics simulations. Overall, MTSSMol provides an efficient algorithmic framework that significantly enhances molecular representation learning and aids in the screening of potential drug candidates, offering an important tool for accelerating the drug discovery process.

Professor Ts Dr. Nor Azman Ismail

Universiti Teknologi Malaysia (UTM), Malaysia

Professor Ts Dr. Nor Azman Ismail became a Member of IEEE in 2017. He has been an active member of the computing research community for over 25 years, contributing significantly to academia. Throughout his distinguished career, Dr. Nor Azman has held various leadership positions, including Deputy Dean (Research and Innovation), Associate Chair (Research and Academic Staff), Deputy Director of Corporate Affairs and Head of Virtual Visualization Vision (ViCubeLab) Research Group. His administrative roles have spanned from 2008 to 2018, showcasing his commitment to shaping the strategic direction of research and innovation at different levels, nationally and internationally.

Dr. Nor Azman's research interests span Multimodal Interaction, UI/UX experimentation, Image Retrieval, Web Mining, Human-Centric AI, and Computer Vision. His commitment to advancing knowledge and fostering industry collaboration underscores his dedication to pushing the boundaries of computing research.

Currently holding the position of Associate Professor at the Faculty of Computing, UTM, Johor Bahru, Dr. Nor Azman is an esteemed member of professional societies, including IEEE and Association for Computing Machinery (ACM) SIGCHI Chapter. His contributions extend to serving on IEEE committees and publications, showcasing his commitment to advancing the field.

Title: Human-Centred AI Systems for Improving Personal Health Self-Care: A Personalized and Supportive Approach to Digital Health Interventions

Abstract:

Personal health self-care often requires individuals to make continuous decisions, track habits, 

and stay motivated, yet most digital health tools still deliver broad, generic guidance that does 

not reflect each person’s unique lifestyle or evolving needs. This limits user engagement and 

reduces the long-term effectiveness of digital health interventions. This keynote introduces a 

human-centred AI approach that brings together behavioural insights, thoughtful design, and 

adaptive intelligence to create digital health systems that feel more personal, supportive, and 

responsive to real-life challenges. Rather than relying on one-size-fits-all features, the approach 

adapts to individuals over time, offering tailored support that enhances motivation and 

confidence in managing health routines. Using diabetes self-care as the main case study, the 

keynote demonstrates how integrating behavioural science, HCI principles, and AI-driven 

adaptivity can significantly improve user experience and outcomes. User studies with both 

experts and everyday individuals showed strong usability and positive engagement, confirming 

that personalized and supportive systems can outperform traditional digital health tools.

Overall, this work highlights a scalable pathway for designing next-generation digital health 

interventions solutions that understand people, adapt to their behaviours, and help them take 

better control of their health and well-being.

Prof. Azlan bin Mohd Zain

Universiti Teknologi Malaysia, Malaysia

He received the Ph.D. degree in computer science from Universiti Teknologi Malaysia (UTM), in 2010. He was appointed as the Director of the Big Data Center, UTM, in April 2020. He is currently an Associate Professor with the School of Computing, UTM. His main research interests include artificial intelligence, modeling and optimization, machining, and statistical process control.

Title: THE ROLE OF ARTIFICIAL INTELLIGENCE IN COMPUTER VISION

Abstract: The topic of artificial intelligence (AI) and computer vision is covered in this sharing session. Artificial Intelligence is a technique that allows machines and computers to perform computer vision tasks intelligently. A subset of artificial intelligence (AI) called machine learning (ML) uses algorithms to provide AI applications. A subset of machine learning (ML) called deep learning (DL) is used to tackle increasingly challenging computer vision tasks. In this session, the significance of machine learning and deep learning for computer vision tasks such object recognition, object localization, segmentation, detection, and classification of images is discussed. This session concludes with a demonstration of a small computer vision project that uses an AI tool to detect image edges.

Prof. Xuehe Wang

Sun Yat-sen University, China

Prof. Xuehe Wang obtained her Ph.D. in electrical and electronic engineering from Nanyang Technological University, Singapore in 2016. 

She currently holds the position of Associate Professor at the School of Artificial Intelligence, Sun Yat- sen University, China. Prior to this role, she served as an Assistant Professor at the Infocomm Technology Cluster within Singapore Institute of Technology from 2019 to 2021, and as a postdoctoral research fellow at the Pillar of Engineering Systems and Design, Singapore University of Technology and Design from 2015 to 2019. Her research interests encompass multi-agent systems, federated learning, network economics, and game theory.

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