Yoshua Bengio is a Canadian researcher specializing in artificial intelligence, and a pioneer in deep learning. He was born in France in 1964, studied in Montreal, obtained his PhD in computer science from McGill University in 1991 and completed post-doctoral studies at MIT. Since 1993, he has been a professor in the Department of Computer Science and Operational Research at the Université de Montréal. He is also Scientific Director of Mila, Scientific director of IVADO and Canada Research Chair in Statistical Learning Algorithms, a Canada CIFAR AI Chair and Co-Director of the CIFAR Learning in Machines & Brains program.
In 2019, he received the ACM A.M. Turing Prize, considered the “Nobel Prize of Computer Science” along with Geoffrey Hinton and Yann LeCun for their advances in conceptual foundations and engineering that have made deep neural networks an essential component of computer science.
His main research ambition is to understand principles of learning that yield intelligence. He supervises a large group of graduate students and postdocs. His research is widely cited (over 250 000 citations found by Google Scholar in January 2020, with an H-index over 150, and rising fast).
Aaron Courville is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal, and member of Mila — Quebec Artificial Intelligence Institute and a Canada CIFAR AI Chair. His current research interests focus on the development of deep learning models and methods. He is interested in developing probabilistic models and novel inference methods. His applications mainly focus on computer vision, however he is also interested in domains such as natural language processing, audio signal processing, speech understanding and just about any other artificial-intelligence-related task.
Pierre-Luc Bacon is an assistant professor at University of Montreal's DIRO. He is also a member of Mila Quebec, the Institute for Data Valorization (IVADO) and holds a Facebook CIFAR AI chair. He obtained his PhD in computer science at McGill University and pursued a postdoc at Stanford University. His research is broadly concerned by the problem of learning to take decisions over long time spans and its ramifications in optimization and representation learning.