题目：Advanced AI for Time Series Sensor Data Analytics
报告人：Xiaoli Li
时间：3月31日 下午2：30
地点：计算机学院101会议室
Abstract:
The rapid proliferation of sensors across manufacturing, aerospace, healthcare, and other sectors is creating unprecedented opportunities alongside significant analytical challenges for understanding time series data. Traditional approaches often struggle to keep pace with the scale, complexity, and real-time demands of applications such as predictive maintenance, equipment health monitoring, and operational optimization.
This talk explores cutting-edge artificial intelligence techniques that are transforming how sensor data is interpreted and utilized. It will cover self-supervised representation learning for extracting robust features from largely unlabeled time series, as well as unsupervised domain adaptation to address distribution shifts in multivariate sensor streams. In addition, the talk will discuss model compression and optimization strategies that enable low-latency, edge-level intelligence.
The presentation will further examine the emerging role of time-series foundation models and their potential to unify diverse tasks, streamline analytical workflows, and unlock new application frontiers. Through real-world case studies and recent research advances, the talk will demonstrate how next-generation AI methods are enhancing predictive analytics and driving transformative innovation across industrial systems.
Bio:
Xiaoli Li is a Full Professor and Head of the Information Systems Technology and Design pillar at the Singapore University of Technology and Design (SUTD). He previously led the Machine Intellection Department at A*STAR, 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 artificial intelligence, data mining, machine learning, and bioinformatics. He has authored over 400 peer-reviewed publications, with more than 40,000 citations and an h-index of 92, and has received over ten best paper awards. He currently serves as Editor-in-Chief of the Annual Review of Artificial Intelligence and as an Associate Editor for leading journals, including IEEE Transactions on Artificial Intelligence and Knowledge and Information Systems. He has also held key leadership roles as conference chair or area chair at premier venues such as AAAI, IJCAI, ICLR, NeurIPS, KDD, and ICDM.
Beyond academia, he has extensive industry engagement experience, having established and led multiple joint laboratories and spearheaded numerous large-scale R&D collaborations with global partners across aerospace, telecommunications, insurance, and professional services.
His contributions have been widely recognized, including being named among the world‘s top 2% scientists in AI by Stanford University and as a Clarivate Highly Cited Researcher.
