•   When: Wednesday, October 08, 2025 from 11:10 AM to 12:10 PM
•   Speakers: Dr. Jessica Lin
•   Location: ENGR 4201
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Abstract

Massive volumes of time series are generated daily across disciplines, from sensors and industrial systems to healthcare and environmental monitoring. Yet, the unique characteristics of time series make them notoriously challenging for traditional data mining and machine learning approaches. In this talk, I will present an overview of my research in time series data mining, from fundamental advances in data representation and similarity search to the discovery of meaningful, non-trivial patterns. 

Two major themes of my work are the discovery of recurrent patterns in time series, known as time series motifs — a concept we introduced in an early research — and the detection of in-sequence anomalies. Time series motifs capture fundamental local conservation mechanisms that persist even in noisy and nonstationary data, and thus offer insights into the physical systems that generate them. Anomalies, in contrast, reveal rare deviations that often carry critical and actionable information in a broad range of applications such as healthcare, equipment safety, security surveillance, and fraud detection. I will discuss the key challenges in time series motif discovery and anomaly detection, and our contributions to overcoming them in supporting the development of more robust, transparent, and interpretable models.

Speaker Bio

Dr. Jessica Lin is an Associate Professor in the Department of Computer Science at George Mason University. She received her PhD in Computer Science from the University of California, Riverside, in 2005. Dr. Lin’s research interests focus on the mining of time series and spatiotemporal databases. More specifically, she has published work on anomaly detection, frequent pattern (motif) discovery, contrast set, and rule mining, clustering, and visualization on time series and spatiotemporal data. She has worked on several collaborative projects in diverse disciplines, including medicine, activity recognition, national security, CPU manufacturing, finance, geoinformatics, and meteorology, with support from the National Science Foundation (NSF), Missile Defense Agency (MDA), National Institutes of Health (NIH), Sandia National Laboratories/Department of Energy (DOE), U.S. Army, Naval Research Lab (NRL), and Intel Corporation. Dr. Lin has served as an Area Chair or PC member in top-tier data mining conferences, including KDD, PKDD/ECML, SDM, ICDM, and CIKM. She is the co-Editor-in-Chief for the Big Data Research Journal, and Associate Editors for the Knowledge and Information Systems Journal (KAIS), ACM Transactions on Intelligent Systems and Technology (ACM TIST), and Pattern Recognition Journal.