Professor Song

Abstract: 

Event detection has been a significant topic for a long time. Quickest event detection, which refers to real-time detection of abrupt changes in the behavior of an observed event as quickly as possible after they occur, is essential. AI/Machine Learning has the potential to achieve quickest detection. However, AI/Machine Learning lacks the necessary mathematical framework to provide guarantees on correctness. The integration of AI/Machine Learning with quickest detection not only creates new research opportunities with major societal implications, but also poses new research challenges. In this lecture, I will present a comprehensive survey of existing literature in the emerging area of AI/Machine Learning for quickest event detection, identify the challenges, and evaluate the trends. I will also introduce our research findings in this area.

Biography:

Houbing Herbert Song (M’12–SM’14-F’23) received the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, VA, in August 2012. He is currently a Professor, the Founding Director of the NSF Center for Aviation Big Data Analytics (Planning), the Associate Director for Leadership of the DOT Transportation Cybersecurity Center for Advanced Research and Education (Tier 1 Center), and the Director of the Security and Optimization for Networked Globe Laboratory (SONG Lab, www.SONGLab.us), University of Maryland, Baltimore County (UMBC), Baltimore, MD. He is a Distinguished Visiting Fellow of the Scottish Informatics and Computer Science Alliance (SICSA). Prior to joining UMBC, he was a Tenured Associate Professor of Electrical Engineering and Computer Science at Embry-Riddle Aeronautical University, Daytona Beach, FL. He serves as an Associate Editor for IEEE Transactions on Artificial Intelligence (TAI) (2023-present), IEEE Internet of Things Journal (2020-present), IEEE Transactions on Intelligent Transportation Systems (2021-present), and IEEE Journal on Miniaturization for Air and Space Systems (J-MASS) (2020-present). He was an Associate Technical Editor for IEEE Communications Magazine (2017-2020). He is the editor of ten books, the author of more than 100 articles and the inventor of 2 patents. His research interests include AI/machine learning/big data analytics, cyber-physical systems/internet of things, and cybersecurity and privacy. His research has been sponsored by federal agencies (including National Science Foundation, National Aeronautics and Space Administration, US Department of Transportation, and Federal Aviation Administration, among others) and industry. His research has been featured on popular news media outlets, including IEEE Spectrum, IEEE GlobalSpec's Engineering360, IEEE Transmitter, insideBIGDATA, Association for Uncrewed Vehicle Systems International (AUVSI), Security Magazine, CXOTech Magazine, Fox News, U.S. News & World Report, The Washington Times, and New Atlas.  

Dr. Song is an IEEE Fellow, an Asia-Pacific Artificial Intelligence Association (AAIA) Fellow, an ACM Distinguished Member, and a Full Member of Sigma Xi. Dr. Song has been a Highly Cited Researcher identified by Web of Science since 2021. He is an ACM Distinguished Speaker (2020-present), an IEEE Computer Society Distinguished Visitor (2024-present), an IEEE Communications Society (ComSoc) Distinguished Lecturer (2024-present), an IEEE Intelligent Transportation Systems Society (ITSS) Distinguished Lecturer (2024-present), an IEEE Vehicular Technology Society (VTS) Distinguished Lecturer (2023-present) and an IEEE Systems Council Distinguished Lecturer (2023-present). Dr. Song received Research.com Rising Star of Science Award in 2022, 2021 Harry Rowe Mimno Award bestowed by IEEE Aerospace and Electronic Systems Society, and 10+ Best Paper Awards from major international conferences, including IEEE CPSCom-2019, IEEE ICII 2019, IEEE/AIAA ICNS 2019, IEEE CBDCom 2020, WASA 2020, AIAA/ IEEE DASC 2021, IEEE GLOBECOM 2021 and IEEE INFOCOM 2022. He has been an IEEE Impact Creator since 2023. 

Email:

Address:Department of Information Systems, University of Maryland, Baltimore, United States, MD 21250