Distinguished Seminar on Agentic AI
⸺ Agentic AI marks a significant advancement in artificial intelligence, transitioning from passive
tools to proactive, collaborative partners. Unlike earlier models that merely responded to prompts or
categorized data, these next-generation systems are characterized by their ability to set goals, plan
strategies, and execute tasks autonomously. They employ sophisticated reasoning methods to
deconstruct complex objectives into actionable steps, leveraging other software, databases, and APIs to
complete tasks with minimal human input. This shift from static information processing to dynamic, goaldriven
action introduces a new approach to problem-solving, allowing AI agents to manage complex
projects, conduct research, and offer comprehensive solutions. This evolution fundamentally transforms
human-machine collaboration and paves the way for the next generation of intelligent systems. In clinical
settings, agentic AI is being developed as a diagnostic partner, analyzing patient history, symptoms, and
research to suggest diagnoses and testing. It also streamlines administrative tasks like prior
authorizations, scheduling, and encounter summaries. In personalized medicine, dynamic care plans can
be created and managed for chronic disease patients. These plans adjust treatments based on real-time
data from wearables and patient feedback, fostering a more proactive, efficient, and patient-centred
healthcare system.
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Pr. Fakhri Karray
Pr. Fakhri Karray is the inaugural co-director of the University of Waterloo Artificial Intelligence Institute and served as the Loblaws Research Chair in Artificial Intelligence in the department of electrical and computer engineering at the University of Waterloo, Canada. He is also Professor of Machine Learning and held the position of Provost at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a research-oriented artificial intelligence (AI) graduate institution in Abu Dhabi, UAE. Fakhri’s research focuses on operational and generative AI, cognitive machines, natural human-machine interaction, and autonomous and intelligent systems, with applications to virtual care systems, cognitive and self-aware devices, and predictive analytics in supply chain management and intelligent transportation systems. He holds editorial roles in major publications related to intelligent systems and information fusion. Fakhri’s latest textbook, “Elements of Dimensionality Reduction and Manifold Learning,” was published by Springer Nature in early 2023. In 2021, he was honored by the IEEE Vehicular Technology Society (VTS) with the IEEE VTS Best Land Transportation Paper Award for his pioneering research on enhancing traffic flow prediction using deep learning and AI. Furthermore, his research on federated learning in communication systems earned him and his co-authors the 2022 IEEE Communication Society’s MeditCom Conference Best Paper Award. He holds fellowship status in the IEEE, the Canadian Academy of Engineering, and the Engineering Institute of Canada. Additionally, he has served as a Distinguished Lecturer for the IEEE and is a Fellow of the Kavli Frontiers of Science. Fakhri earned his Ph.D. from the University of Illinois Urbana-Champaign, USA.
Speaker :
Pr. Fakhreddine Karray
Date :
30 October 2025.
Times :
1 pm
English 
French