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Special Session 2:
Neurocognitive-Inspired Machine Learning for Adaptive, Robust, and Secure Intelligence

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This special session aims to explore emerging paradigms in machine learning inspired by neurocognitive principles underlying human intelligence. While modern machine learning models have achieved remarkable success, they remain fundamentally limited in adaptability, interpretability, and robustness in dynamic, real-world environments. In addition, these systems are increasingly vulnerable to adversarial manipulation, distribution shifts, and reliability failures, highlighting the need for intrinsically secure and resilient learning mechanisms.

This session focuses on bridging this gap by integrating biologically grounded mechanisms such as memory consolidation, perception-action loops, and predictive processing into machine learning systems. The goal is to move beyond static feedforward architectures toward adaptive, closed-loop intelligence capable of reasoning under uncertainty, learning from limited data, and generalizing across tasks. A key emphasis is placed on embedding security, robustness, and trustworthiness directly into the learning process through neurocognitive principles, enabling models to detect, adapt to, and mitigate anomalous or adversarial conditions.

Scope and topics:

This session is particularly relevant for researchers seeking to develop next-generation AI systems that exhibit human-like adaptability, resilience, and reasoning capabilities. It also targets emerging challenges at the intersection of machine learning and security, where robustness, reliability, and safety are critical for deployment in real-world and high-stakes environments. It aims to foster interdisciplinary collaboration across machine learning, neuroscience, robotics, and cognitive science communities.

Topics of interest include, but are not limited to:

• Perception-action loops and embodied intelligence
• Memory-augmented learning systems (episodic and semantic memory models)
• Multimodal learning integrating vision, touch, and contextual reasoning
• Learning under uncertainty and sparse observations
• Brain-inspired architectures for robotics and autonomous systems
• Human-AI interaction and cognitive modeling
• Interpretability through biologically plausible mechanisms
• Adversarial robustness and resilience in AI systems
• Security-aware learning frameworks and anomaly detection mechanisms
• Cognitive-inspired defenses against adversarial attacks and distribution shifts
• Trustworthy AI through biologically grounded reasoning and uncertainty modeling

Chairs:

• Chair Emails

Chair: Noorbakhsh Amiri Golilarz: noor.amiri@ua.edu
Co-chair: Ahmed Imteaj: aimteaj@fau.edu
Co-chair: Sudip Mittal: sudip.mittal@ua.edu


• Chair Biographies

Noorbakhsh Amiri Golilarz is an Assistant Professor in the Department of Computer Science at The University of Alabama and Director of the BRAINS Lab (Brain-Inspired Autonomous & Intelligent Systems). His research focuses on neurocognitive-inspired AI, multimodal machine learning, computer vision, image processing, and robotics. His work explores biologically grounded architectures integrating perception, attention, memory, and reasoning for adaptive intelligence. Dr. Amiri is the author of numerous papers in the fields of AI, computer vision, and image processing. He has served as a Lead Guest Editor and Topic Editor for several SCI-indexed journals and has also held the role of Conference Program Chair.

Ahmed Imteaj is a Tenure-Track Assistant Professor in the Department of Electrical Engineering and Computer Science at Florida Atlantic University, Boca Raton. He is a Faculty Fellow at the Institute for Sensing and Embedded Network Systems Engineering (I-SENSE) and a senior member of the Center for Connected Autonomy and Artificial Intelligence (CA-AI). He is also the Founding Director of the Secure Prediction, Edge AI and Multimodal LLM Lab (SPEED Lab). His research focuses on developing robust, secure, and efficient AI systems, with particular emphasis on large Vision-Language Models, agentic and quantum AI, federated learning (edge intelligence) and cybersecurity. He is particularly interested in control-theory of Vision-Language Models and distributed intelligence, with applications in autonomous transportation, smart city systems, surveillance, mission-critical operational intelligence, underwater robotics, agriculture, and healthcare. Dr. Imteaj is the recipient of multiple competitive research awards, including the NSF CRII Award, the NSF NAIRR Pilot, DHS CINA and ORAU Research Innovation Partnerships Grant.

Sudip Mittal: is an Associate Professor in the Department of Computer Science at The University of Alabama. His research interests fall broadly in the areas of Cybersecurity, Cyber-Physical Systems, and Artificial Intelligence. More specifically, his work focuses on building self-protecting systems, autonomous intrusion response, along with predictive maintenance and security of unmanned vehicles/aircraft. Mittal has received funding from NSF, NIH, NSA, USAF, USACE, and other U.S. Department of Defense agencies. He has published over 110 journals and conference papers in leading venues. Mittal’s work has been referenced in The LA times, Business Insider, WIRED, and The CyberWire.

Technical Committee

• Dr. Jingdao Chen, Mississippi State University, USA
• Dr. Iman Dehzangi, Rutgers University, USA
• Dr. Hossein Karimi, California State University, Fullerton, USA
• Dr. Soroush Korivand, Mississippi State University, USA
• Dr. Jalil Addeh, Mount Royal University, Canada

Paper Submission Instructions

All papers will be double-blind reviewed and must present original work.

• CMT Submission Site
• Select the track: Special Session 2: Neurocognitive-Inspired Machine Learning for Adaptive, Robust, and Secure Intelligence

Papers submitted for reviewing should conform to IEEE specifications. Manuscript templates can be downloaded from:

• IEEE website

Keydates

• Submission Deadline: June 20, 2026
• Notification of Acceptance: July 10, 2026

Registration

In order for your paper to be published in the proceedings you must register to the conference.

Paper Presentation Instructions

The papers submitted to this track will be presented in person as part of the conference. There is no virtual presentation for this session.