Dr. Houbing Herbert Song

There are three waves of Artificial Intelligence. The first Wave of AI is Crafted Knowledge, which includes rule-based AI systems. The second wave of AI is Statistical Learning, which includes machine becoming intelligent by using statistical methods. The third wave of AI is contextual adaptation. In the third wave, instead of learning from data, intelligent machines will understand and perceive the world on its own, and learn by understanding the world and reason with it. Neurosymbolic AI, which combines neural networks with symbolic representations, has emerged as a promising solution of the third wave of AI. In this talk, I will share my journey from internet of things to incremental learning and transfer learning to neurosymbolic AI; then I will present my perspective on the emerging area with focus on neurosymbolic transfer learning, neurosymbolic reinforcement learning, and neurosymbolic AI verification and validation (testing and evaluation).

Biography:

Houbing Herbert Song (F’23) received the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, VA, in August 2012.

He is currently an Associate Professor, the Founding Director of the NSF Center for Aviation Big Data Analytics, 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 has been the Founding Chair of Trustworthy Internet of Things (TRUST-IoT) Working Group within IEEE IoT Technical Community since 2024. He is a Distinguished Visiting Fellow of the Scottish Informatics and Computer Science Alliance (SICSA). 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 10+ 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, StateTech Magazine, 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, IEEE 2021 Harry Rowe Mimno Award, and 10+ Best Paper Awards from major international conferences. He has been an IEEE Impact Creator since 2023.