Singapore University of Technology and Design
xiaoli_li@sutd.edu.sg
The rapid evolution of artificial intelligence is transforming vast and heterogeneous data into increasingly autonomous and adaptive intelligent systems. Yet a fundamental challenge remains: how can machine learning systems move beyond pattern recognition toward robust intelligence that can learn and adapt effectively in complex, dynamic environments?
In this keynote, I will present a unified perspective on the foundations and frontiers of adaptive machine learning systems that bridge data-driven modeling with the core principles of intelligence science. Drawing on recent advances in representation learning, time-series analytics, and learning under limited supervision, I will discuss how modern learning architectures acquire structured knowledge and continuously adapt from evolving data streams. Particular attention will be given to learning from imperfect and weakly labeled data, a setting that closely reflects the realities of real-world intelligent systems.
Beyond algorithms, I will highlight emerging directions toward system-level intelligence, including life-cycle prediction in complex engineering systems, intelligent sensing, and the integration of learning with decision-making and reasoning. These developments point toward a new generation of adaptive machine intelligence that is resilient, interpretable, and capable of long-term learning.
By connecting foundational machine learning theories with practical intelligent systems, this talk aims to illuminate key opportunities and open challenges at the intersection of artificial intelligence and intelligence science, and to outline a roadmap toward building truly adaptive and trustworthy intelligent systems.
Xiaoli LI is currently a Full Professor and Head of the Information Systems Technology and Design Pillar at Singapore University of Technology and Design (SUTD). He previously led A*STAR’s Machine Intellection Department, where he built and directed Singapore’s largest AI and data science research group. He is also an Adjunct Full Professor at Nanyang Technological University, and a Fellow of both IEEE and AAIA.
His research spans AI, data mining, machine learning, and bioinformatics, and has produced more than 400 peer-reviewed publications with over 30,000 citations, an h-index of 90, and more than ten best paper awards. He serves as Editor-in-Chief of the Annual Review of Artificial Intelligence and as an Associate Editor for leading journals such as IEEE Transactions on Artificial Intelligence and Knowledge and Information Systems. He has also played key leadership roles as conference chair or area chair at premier venues including AAAI, IJCAI, ICLR, NeurIPS, KDD, and ICDM.
Beyond academia, Xiaoli brings extensive industry engagement experience, having established and led multiple joint labs and spearheaded more than ten major R&D collaborations with global partners in aerospace, telecommunications, insurance, and professional services.
His contributions have earned him international recognition as one of the world’s top 2% scientists in AI (Stanford University) and as a Clarivate Highly Cited Researcher.