University Research Chair Professor
University of Waterloo, Canada
Research field: Cognitive robotics, natural human-machine interaction, autonomous and intelligent systems, and voice and media concept extraction.
Biography: Fakhreddine Karray is the University Research Chair Professor in Electrical and Computer Engineering at the University of Waterloo, Canada and the director of the University’s Center for Pattern Analysis and Machine Intelligence. He received the PhD degree from the University of Illinois, Urbana Champaign, USA in the area of systems and control. Karray’s current research interests are in the areas of intelligent systems design, big data analytics, soft computing, sensor fusion, and context aware machines with applications to intelligent transportation systems, Internet of things, cognitive robotics and natural man- machine interaction. He has authored extensively in these areas, technical articles, textbooks and US patents. He has chaired/co-chaired international conferences in his area of expertise and has served as keynote/plenary speaker on numerous occasions. He has also served as the associate editor/guest editor for several journals, including the IEEE Transactions on Cybernetics, the IEEE Transactions on Neural Networks and Learning, the IEEE Transactions on Mechatronics, the IEEE Computational Intelligence Magazine. He is the Chair of the IEEE Computational Intelligence Society Chapter in Kitchener-Waterloo, Canada and chaired various sub- committees of the IEEE Computational Intelligence Society. He received national and international awards, including the Premier Research Excellence Award and the 2014 Pattern Recognition Society Best Paper Award. Karray is the co-founder of two University of Waterloo spin-off companies, specializing in designing and commercializing products for next generation connected cars and man-machine interaction systems. He is a co-founder and past vice president of the Arab Science and Technology Foundation and is current president of the Association for Image and Machine Intelligence.
Talk 1: Advances in Machine Learning: From Pattern Recognition to Deep learning
Interest in developing more powerful and more sophisticated machine learning tools, emanates from the need of processing and mining information from the ever increasing amounts of data that are becoming available to large corporation and government agencies, also known as Big Data. Machine learning is in fact an area that has seen major growth in recent years in par with cloud computing and big data analytics. The talk highlights major aspects of machine learning tools, namely the connectionist based ones and outlines advances made in the field starting in the seventies with pattern recognition and regression modeling to culminate in the last few years with deep learning. The talk also highlights some of the applications pertinent to the field and future growth directions in the area, namely in intelligent transportation systems, traffic prediction, health, energy (smart grids), and urban planning (smart cities design).
Talk 2: Smart Mobility Using Tools of Computational Intelligence
Major advances recently achieved in the fields of machine learning, big data analytics, and cloud computing have opened up new market opportunities in strategic areas such as in transportation, health, energy and urban planning. These are critical components for building sustainable cities of the future better known as smart cities. “Smart mobility” represents a corner stone and an integral part of the smart city concept. It deals with the design of more efficient, more intelligent, and safer transportation and communication systems that are better suited and more adapted to latest advances in information and communication technologies, including 5G networks and Internet of things: IOT. It is expected that most modes of transportation will become soon connected to the cloud and to IoT infrastructure. With more than a billion vehicles on the roads today, a number expected to increase by 250% in 2050, the design of highly efficient and safer transportation systems is becoming a necessity. This is a major challenge for car manufacturers, road infrastructure planners, and transportation policy makers. The talk highlights newly developed technologies allowing for the design of next generation mobility systems. These enabling technologies represent the core of the smart mobility concept and have become prevalent thanks to spectacular advances made in the fields of machine intelligence, smart devices, sensor networks, big data analytics and Internet of things. They allow for the design of more intelligent vehicles, permit safer travel journeys and enable the design of more effective and smarter transportation networks, while significantly reducing traffic congestion, road fatalities and injuries, fuel consumption and pollution. The talk also outlines recent research work carried out at the Center for Pattern Analysis and Machine Intelligence and highlights challenges toward achieving short and long-term goals for building more livable and more sustainable cities of the future.