Prof. Xiaoli Li, IEEE Fellow, Nanyang Technological University, Singapore

BIO: Xiaoli is currently the Department Head and Senior Principal Scientist at the Institute for Infocomm Research, A*STAR, Singapore. He also serves as an adjunct full professor at the School of Computer Science and Engineering, Nanyang Technological University, Singapore. With a diverse range of research interests, Xiaoli focuses on cutting-edge areas such as AI, data mining, machine learning, and bioinformatics. His contributions to these fields are evident through his extensive publication record, boasting over 360 peer-reviewed papers, and the recognition he has received, including over ten best paper awards. He has been serving as Editor-in-chief of the Annual Review of Artificial Intelligence and an Associate Editor for prestigious journals like IEEE Transactions on Artificial Intelligence and Knowledge and Information Systems, as well as conference chairs and area chairs of leading AI, machine learning, and data science conferences, such as AAAI, IJCAI, ICLR, NeurIPS, KDD, ICDM etc. Beyond academia, Xiaoli possesses extensive industry experience, where he has successfully spearheaded over 10 R&D projects in collaboration with major industry players across diverse sectors, such as aerospace, telecom, insurance, and professional service companies. Xiaoli is an IEEE Fellow and Fellow of Asia-Pacific Artificial Intelligence Association (AAIA). He has been recognized as one of the world's top 2% scientists in the AI domain by Stanford University, Clarivate's Highly Cited Researcher,  and one of the top ranked computer scientists by Research.com.

Title: AI-Driven Big Time Series Data Analytics

Abstract: The exponential growth of sensor deployments across industries such as manufacturing, aerospace, transportation, and education presents unprecedented opportunities for leveraging AI in time-series data analytics. This keynote highlights state-of-the-art AI solutions powering real-world applications, including predictive maintenance, and real-time decision-making. In manufacturing and aerospace, AI-driven analytics enhance operational efficiency by enabling predictive maintenance, reducing downtime, and improving machine remaining useful life predictions. Key challenges, such as achieving high accuracy, compressing models for edge deployment, and adapting to diverse industrial domains, will be discussed. In transportation, real-time AI analytics revolutionize smart traffic management, promoting safety and optimizing resource allocation. Meanwhile, in education, the rise of online learning platforms generates vast time-series data, paving the way for personalized and adaptive learning experiences. From knowledge tracing to predicting student performance and identifying at-risk learners, AI enables timely interventions and fosters tailored learning pathways. Join us to explore how AI is revolutionizing industries and education, driving innovation, and enhancing global competitiveness in today’s data-centric era.