Speakers

Prof. Haoran Xie

Lingnan University, China

Prof. XIE Haoran is a Professor and the Person-in-Charge of Division of Artificial Intelligence, the Acting Associate Dean of School of Data Science, and the Director of LEO Dr. David P. Chan Institute of Data Science at Lingnan University, Hong Kong. He is the Editor-in-Chief of Natural Language Processing Journal (Elsevier), Computers & Education: Artificial Intelligence (Elsevier), and Computers & Education: X Reality (Elsevier), Co-Editor-in-Chief of Knowledge Management and E-Learning (HKU) the Associate Editor of International Journal of Machine Learning and Cybernetics (Springer), Array (Elseiver), International Journal of Mobile Learning and Organisation (InderScience). Till Sep 2024, his totally Google Scholar citation counts is 18,647, h-index is 55, and i-10 index is 180 at Google Scholar.  

Research interests

Artificial Intelligence, Big Data, Educational Technologies

Prof. Jiangtao Wang

University of Science and Technology of China, China

Dr. J Wang is a Professor at the School of AI and Data Science, University of Science and Technology of China. Previsouly, he was an Assistant Professor and Associate Professor in the UK. Dr. Wang specializes in developing AI algorithms to analyze complex healthcare data, aiming to improve healthcare delivery. He has developed advanced machine learning models for population health monitoring, diagnosis prediction, drug-drug interaction analysis, and COVID-19 severity estimation.

Title: Incorporating Domain Knowledge to Build Health Intelligence on Small Datasets: Vision and Case Studies

Abstract: Artificial Intelligence (AI) is transforming healthcare by enabling predictive models, automated diagnostics, and personalized treatment plans. However, realizing the full potential of AI in health faces a significant challenge: the scarcity of high-quality labeled data. Many healthcare applications, such as disease prediction, drug interaction analysis, and public health monitoring, require vast amounts of data to train accurate models. Yet, health data is often limited, fragmented, or difficult to obtain due to privacy concerns, patient diversity, and the complexity of medical conditions. This data challenge poses a critical barrier to advancing AI-driven solutions in healthcare. In this talk,Iexplore how domain knowledge—such as medical expertise, clinical guidelines, and epidemiological insights—can play a crucial role in addressing this issue. By incorporating expert knowledge into AI models, we can enhance learning from small datasets and generate more reliable predictions. Through case studies in population health, we will demonstrate practical applications, such as the use of expert-informed models for predicting health outcomes and assessing environmental health risks. These examples showcase how combining AI techniques with domain knowledge not only helps overcome the data limitation but also produces actionable insights for real-world health challenges.

Prof. Yong Luo

Wuhan University, China

Yong Luo received the B.E. degree in Computer Science from the Northwestern Polytechnical University, Xi’an, China, and the D.Sc. degree in the School of Electronics Engineering and Computer Science, Peking University, Beijing, China. He was a Research Fellow with the School of Computer Science and Engineering, Nanyang Technological University, and is currently a Professor with the School of Computer Science, Wuhan University, China. His research interests are primarily on machine learning and data mining with applications to visual information understanding and analysis. He has authored or co-authored over 100 papers in top journals and prestigious conferences including Nature Machine Intelligence, Nature Communications, IEEE T-PAMI and IJCV. He is serving on editorial board for IEEE T-MM. He received the IEEE Globecom 2016 Best Paper Award, and was nominated as the IJCAI 2017 Distinguished Best Paper Award. He is also a corecipient of the IEEE TMM 2023, IEEE ICME 2019 and IEEE VCIP 2019 Best Paper Awards.

Dr. Xin Li

Vice president of the AI Research Institute of iFLYTEK

Iflytek Co.,Ltd, China

Li Xin, Ph.D. and the senior engineer, is the vice president of the AI Research Institute and the head of the R&D department of iFLYTEK. He obtained his Ph.D. degree from and served as postdoctoral researcher and associate professor at the University of Science and Technology of China（USTC）, and was a visiting scholar at the University of Technology Sydney（UTS）. He is also a researcher at the National Key Laboratory for Cognitive Intelligence, a senior member of China Computer Federation（CCF），the member of the Executive Committee of the CCF's Big Data Committee, the member of council in the China Association of Standardization（CAS），the deputy director of Brain-Computer Interface and Brain-inspired Intelligence Special Committee of CAS, and the vice chairman of the System and Industry Application Group in Brain-Computer Interface Alliance（BCIA）. He is also a young expert of the China Internet Society, a standing director of Anhui Artificial Intelligence Society and a founding editor of the Journal of Natural Language Processing, and received “The Young 30” honor of Brain Science and Brain-like Intelligence, KSEM, CIKM, SDM best paper/ runners up award. He is mainly responsible for the application research of artificial intelligence and cognitive neuroscience technology in education, medical and other fields. He has led and participated in multiple projects including the strategic leading project of the CAS, 2030 Program and the key research and development programs of the Ministry of Science and Technology of China, along with several funds of Natural Science Foundation of China. He has published over 110 papers and patents in top international academic conferences and well-known journals.