UMBC Assistant Professor Manas Gaur and University of South Carolina professor Amit Sheth have published a new book on Knowledge-Infused Learning. The technique was the topic of Dr. Gaur's dissertation and addresses the opacity of current "black-box" AI models by combining data-driven machine learning techniques with the structured insights of symbolic AI. The book introduces the techniques of neurosymbolic AI, which blends statistical models with symbolic knowledge to make AI safer and user-explainable. Dr. Gaur will teach a combined undergraduate and graduate special topics class on Neurosymbolic AI and Knowledge-infused Learning in UMBC's Fall 2026 semester (MoWe 2:30-3:45).
]]>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 machines 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 their own, and further learn by reasoning with that knowledge. Neurosymbolic AI, which combines neural networks with symbolic representations, has emerged as a promising solution to the third wave of AI. In this talk, I will share my perspective on the emerging area.
Houbing Song is an Associate Professor in the UMBC Information Systems Department and serves as the Director of the NSF Center for Aviation Big Data Analytics (Planning) and the Security and Optimization for Networked Globe Laboratory (SONG Lab). Prior to joining UMBC, he was an Associate Professor of Electrical Engineering and Computer Science at Embry-Riddle Aeronautical University, Daytona Beach, FL. Dr. Song is an IEEE Fellow, an ACM Distinguished Member, and an ACM Distinguished Speaker. Dr. Song has been a Highly Cited Researcher identified by Clarivate (2021, 2022) and a Top 1000 Computer Scientist identified by Research.com. He received Research.com's Rising Star of Science Award in 2022 (World Ranking: 82; US Ranking: 16). Dr. Song was a recipient of 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.