SLAI Seminar 33rd Session will be discussing the topic on "Advanced AI for Time Series Sensor Data Analytics",  from 2:30pm-4pm, April 2 (Thursday) at Room B401, online participation is welcome.

 (Tencent Meeting ID: 223-766-738)

About the Speaker:

Xiaoli 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 40,000 citations, an h-index of 92, 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.

Abstract:

The rapid proliferation of sensors across manufacturing, aerospace, healthcare, and other sectors presents unprecedented opportunities and new analytical challenges, for understanding time series data. Traditional methods struggle to keep pace with the scale, complexity, and real-time demands of predictive maintenance, equipment health monitoring, and operational optimization.

This talk will explore cutting-edge artificial intelligence techniques that are transforming how we interpret and leverage sensor data. Topics will include self-supervised representation learning for extracting robust features from unlabeled time series, unsupervised domain adaptation to address distribution shifts in multivariate sensor streams, and model compression strategies that enable low-latency, edge-level intelligence. The talk will also examine the emerging role of time-series foundation models, their potential to unify diverse tasks, streamline workflows, and unlock powerful new applications. Through real-world case studies and research insights, the presentation will illustrate how next-generation AI methods are enhancing predictive analytics and driving a fundamental transformation in industrial operations and innovation.