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)

报告主题：用于时间序列传感器数据分析的高级人工智能

时间：2026年4月2日（周四）下午14:30-16:00

地点: 深圳河套学院B401教室

线上参与：腾讯会议号223-766-738

主讲嘉宾：李晓黎教授

主持人：权小军教授

讲者简介About the Speaker:

李晓黎现任新加坡科技设计大学（SUTD）信息系统技术与设计学院院长。此前，他曾担任新加坡科研局（A*STAR）机器智能研究所所长，领导新加坡最大的人工智能与数据科学研究团队。他同时也是南洋理工大学计算机科学与工程学院的兼职正教授，并当选为IEEE 会士和亚太人工智能协会（AAIA）会士。他的研究领域涵盖人工智能、数据挖掘、机器学习和生物信息学，已发表400余篇国际同行评审论文，引用次数超过40,000次，h指数为92，并获得十余项国际最佳论文奖。他现任《人工智能年度综述》（Annual Review of Artificial Intelligence）主编，并担任包括《IEEE Transactions on Artificial Intelligence》和《Knowledge and Information Systems》在内的国际知名期刊副主编。同时，他还在AAAI、IJCAI、ICLR、NeurIPS、KDD、ICDM等国际顶级会议中担任大会主席或领域主席等学术职务。在学术之外，李晓黎也拥有丰富的产业经验，曾主持建立多个产学研联合实验室，并与航空、电信、保险及专业服务等多个行业的国际合作伙伴共同主导了10余项重大研发项目。贡献，他被斯坦福大学评选为全球人工智能领域前2%顶尖科学家，并入选科睿唯安全球高被引科学家。

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.

报告摘要：

传感器在制造业、航空航天、医疗等领域的快速普及，既为理解时间序列数据带来了前所未有的机遇，也带来了新的分析挑战。传统方法难以跟上预测性维护、设备健康监测和运营优化等领域对规模、复杂性及实时性的要求。

本次报告将探讨如何利用前沿人工智能技术，革新解读和运用传感器数据的方式。报告主题包括：通过自监督表示学习从未标注的时间序列中提取鲁棒特征；利用无监督领域自适应技术应对多变量传感器流中的数据分布偏移，以及通过模型压缩策略实现低延迟的端侧智能。报告还将探讨时序基础模型这一新兴方向，以及它们统一多样化任务、简化工作流程、开启强大新应用场景的潜力。结合实际案例与研究洞见，本次报告将展示新一代人工智能方法如何提升预测性分析能力，并推动工业运营与创新的根本性变革。

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.