AI is entering an era in which intelligent systems will increasingly perceive, reason, decide, and act in the physical and digital worlds. From autonomous vehicles and intelligent infrastructure to cybersecurity and next-generation Internet of Things, the question is no longer simply whether AI can be made more capable, but whether it can be trusted with consequential decisions. Today’s predominantly data-driven AI systems remain limited by fundamental challenges in explainability, robustness, security, verifiability, accountability, and alignment with human values.

This distinguished lecture will present a vision for neuro-symbolic AI as a foundation for the next generation of trustworthy intelligent systems. By integrating the adaptive learning and perceptual capabilities of neural networks with the structured knowledge, logical reasoning, causal understanding, and formal guarantees of symbolic AI, neuro-symbolic systems can move beyond the limitations of purely statistical intelligence.

The talk will explore how this paradigm can enable AI systems that not only learn from data, but also understand context, reason about consequences, explain their decisions, detect contradictions, resist adversarial manipulation, and operate within explicit safety and ethical constraints. Drawing on examples from trustworthy AI, cybersecurity, autonomous systems, IoT, and cyber-physical systems, the lecture will examine both transformative opportunities and grand challenges. Ultimately, it will advance a vision of AI that is not merely powerful, but verifiably trustworthy—intelligent systems designed to earn, sustain, and deserve human trust.

Speaker: Houbing Herbert Song, IEEE Fellow & Co-EiC of IEEE TII

Short Bio: Houbing Herbert Song is a Tenured Full Professor and Director of the Security and Optimization for Networked Globe (SONG) Laboratory at the University of Maryland, Baltimore County (UMBC). He received his Ph.D. in Electrical Engineering from the University of Virginia in 2012 and is an elected member of the European Academy of Engineering (EAE), an IEEE Fellow and ACM Distinguished Member.

His research focuses on neuro-symbolic AI, trustworthy artificial intelligence, anomaly detection, Artificial Intelligence of Things (AIoT), autonomous systems, and cyber-physical systems. His work has been widely supported by NSF, NASA, USDOT, and FAA, and has been featured in major media outlets including IEEE Spectrum and U.S. News & World Report.

Dr. Song serves as Co-Editor-in-Chief of IEEE Transactions on Industrial Informatics, Area Editor of IEEE Internet of Things Journal, and holds leadership roles in multiple IEEE and ACM initiatives, including founding the ACM EIGTRUST and IEEE TRUST-IoT Working Group. He has also served as general chair and technical program chair for numerous flagship international conferences.

He is an ACM Distinguished Speaker, IEEE Computer Society Distinguished Visitor, and IEEE Distinguished Lecturer across multiple societies. His honors include the IEEE Harry Rowe Mimno Award, Research.com Rising Star of Science Award, multiple best paper awards, and recognition as a Highly Cited Researcher.