Talk Title: When Things Begin to Think: Brain-Inspired AIoT and the Future of Adaptive Intelligence

Abstract:

The next generation of the Internet of Things will not simply connect devices, it will need to perceive, learn, adapt, and make intelligent decisions in complex and continuously changing environments. Interestingly, many of the capabilities we seek in intelligent connected systems already exist in the most sophisticated adaptive system we know: the human brain. This keynote explores the convergence of Artificial Intelligence, IoT, and neuroscience, asking what connected intelligent systems can learn from the brain’s remarkable ability to detect subtle signals, predict what happens next, adapt through experience, and operate efficiently under uncertainty. Drawing from brain computer interface (BCI), neural signal processing, machine learning, and intelligent systems research, the talk introduces a vision of brain-inspired AIoT in which intelligence moves beyond centralized prediction toward distributed, context-aware, adaptive decision making. The keynote will explore applications across wearable and medical IoT, edge intelligence, cyber physical systems, smart environments, and cybersecurity, including how cognitive principles can help systems identify behavioral anomalies, reduce false positives through context, and adapt to previously unseen conditions and threats. It will also examine the growing role of human-AI collaboration, where intelligent devices augment rather than replace human judgment. Ultimately, the talk presents a shift from an Internet of connected Things to an Internet of adaptive Intelligence, where AIoT systems do more than sense and communicate — they continuously learn from their environments, anticipate change, and intelligently adapt to the world around them.

Biography

Dr. Zag ElSayed is an Assistant Professor in the School of Information Technology at the University of Cincinnati and a Research Affiliate at Cincinnati Children’s Hospital Medical Center. His interdisciplinary research integrates brain computer interface (BCI), EEG signal analysis, artificial intelligence (AI), industrial IoT solutions, and cybersecurity with applications in mental health, biomedical signal processing, and smart healthcare systems. Dr. ElSayed earned his Ph.D. in Computer Engineering from the University of Louisiana and holds multiple M.Sc. and advanced degrees in computer science, computer engineering, and automatic control engineering, as well as studies in neuroinformatics. He is a scientist with 12+ years of Industrial IT solutions experience. His scholarly work focuses on cybersecurity, automation, machine learning (ML), EEG-based neural decoding, adaptive AI models for cognitive and neurological disorders, motor rehabilitation, and the development of secure, low-power intelligent systems. He has published many interdisciplinary peer-reviewed articles, and serves as an IEEE Senior Member, keynote speaker, reviewer for NSF and leading international journals and conferences. As an educator, Dr. ElSayed designs and leads graduate and undergraduate courses in data-driven cybersecurity, infrastructure sustainability, and neural systems design, known for integrating cutting-edge technologies with hands-on, experiential learning.