Professor

Loblaws Research Chair in Artificial Intelligence
Department of Electrical and Computer Engineering

Co-Director
The Waterloo Artificial Intelligence Institute
 

Fakhri is the Loblaws Research Chair Professor in Artificial Intelligence in the department of electrical and computer engineering and is the founding co-director of the Waterloo AI Institute. He is the co-author of a textbook on applied artificial intelligence: Soft Computing and Intelligent Systems Design, Addison Wesley Publishing, 2004. He has authored extensively in his field of research (whether applied or theoretical), and has been issued 20 patents (US registered). He is the Associate Editor (AE) of the IEEE Transactions on Cybernetics, the IEEE Transactions on Neural Networks and Learning, and served as AE for the IEEE Transactions on Mechatronics, the IEEE Computational Intelligence Magazine. He also serves on the editorial board of the Elsevier Journal of Information Fusion, International Journal of Robotics and Automation, the Journal of Control and Intelligent Systems, and the Journal of Advances in Artificial Intelligence. Recent work of Fakhri and his research team's work on deep learning-based driver behaviour recognition and prediction has been featured on The Washington Post, Wired Magazine, Globe and Mail, CBC radio and Canada's Discovery Channel. He is a Fellow of the IEEE, a Fellow of the Canadian Academy of Engineering, a Fellow of the Engineering Institute of Canada and the President of the Association for Image and Machine Intelligence. He served as a Distinguished Lecturer for the IEEE and is a Fellow of the Kavli Frontiers of Science (a major research and symposium program of the US National Academy of Sciences) 

Recent areas of research include:

  • Operational artificial intelligence and machine learning
  • Predictive analytics with application to virtual  care
  • Multi-sensor data fusion
  • Cognitive robotics and autonomous machines
  • Smart mobility and big data analytics
  • Concept extraction and natural speech understanding