Enabling Trustworthy Systems through Neuro-Symbolic AI with Applications in Communications and Networking

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Special Presentation by Houbing Herbert Song (U. of Maryland, USA)

Hosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group

Date/Time: Thursday, 17 September 2026 @ 12:00 UTC (8 AM EDT)

Topic:

Enabling Trustworthy Systems through Neuro-Symbolic AI with Applications in Communications and Networking 

Abstract:

AI is increasingly embedded in communications, networking, cybersecurity, autonomous systems, and the Internet of Things, making trustworthiness essential for consequential decisions. Yet, data-driven AI remains challenged by explainability, robustness, security, verifiability, and alignment with human values. This talk presents neuro-symbolic AI as a foundation for trustworthy intelligent systems, combining neural learning and perception with symbolic knowledge, logical reasoning, and explicit constraints. This integration offers a pathway toward AI systems that can learn from data while reasoning about context and consequences, explaining decisions, detecting contradictions, resisting adversarial manipulation, and operating within safety and security boundaries. The talk will highlight applications in communications and networking, with particular emphasis on NeuroBA: Neuro-Symbolic Bitrate Adaptation for IRS-Aided Mobile Video Streaming, demonstrating how neuro-symbolic learning and reasoning can enhance the intelligence, adaptability, robustness, and trustworthiness of AI-enabled communication systems. Ultimately, the talk advances a vision of communications and networking systems that are not merely intelligent and efficient, but trustworthy by design — systems that can learn, reason, adapt, and make decisions worthy of human trust.

Speaker:

Dr. 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.

Brochure (PDF): Webinar-AIML-2026-09-17-Song-TrustNeuroSymbolicAI-Brochure.pdf