Data-Driven Anomaly Detection and Prediction in Public Transportation Networks

Speaker: Prof. Phone Lin, IEEE Fellow，National Taiwan University Time: November 4, 2026, 20:00 (Beijing time) Zoom: https://unsw.zoom.us/j/84840084069 Abstract: Anomaly detection and prediction typically require extensive domain knowledge to develop tools capable of automatically identifying or forecasting anomalous events or behaviors in IoT systems. These systems-particularly those within Public Transportation Networks-often consist of devices with diverse capabilities, functions, and operational lifespans, making the detection of rare anomalies especially challenging. Moreover, establishing domain expertise and collecting a sufficient number of data points for anomaly detection is frequently timeconsuming and costly. Bio: Phone Lin (Fellow, IEEE) received the B.S. and Ph.D. degrees in computer science and information engineering from National Chiao Tung University, Hsinchu, Taiwan, in 1996 and 2001, respectively. He is a Professor with National Taiwan University, Taipei, Taiwan, holding a professorship within the Department of Computer Science and Information Engineering, Graduate Institute of Biomedical Electronics and Bioinformatics, and Internet of Things Research Center, College of EECS, and Graduate Institute of Medical Device and Imaging of College of Medicine. Dr. Lin was a recipient of many prestigious research awards, such as the Outstanding Research Award, Ministry of Science and Technology, Taiwan, in 2016, and the Best Young Researcher of IEEE ComSoc Asia-Pacific Young Researcher Award in 2007. He was the Chair of IEEE Vehicular Technology Society Taipei Chapter from 2014 to 2015. He serves on the editorial boards of several journals, such as IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY.