Radio-Canada has selected Yoshua Bengio, a professor in the Department of Computer Science and Operations Research and head of the Montreal Institute for Learning Algorithms, as the 2017 Scientist of the Year.
This honour was bestowed on Yoshua Bengio this morning at an awards ceremony held at the Maison de Radio-Canada, in the presence of Michel Bissonnette, executive vice-president of French services for Radio-Canada, and Marie-Josée Hébert, UdeM’s vice-rector of research, discovery, creation and innovation, along with other distinguished guests.
“Thanks to Yoshua Bengio, global tech giants have recognized Quebec as a hub of expertise in the field of information technology and the Internet,” Blondin said. “He has helped lay the foundation for the emergence of exhilarating developments in this area for Montreal and Quebec, not to mention nurturing an impressive pool of talent here over the years.”
“There is absolutely no doubt that Yoshua Bengio stands as one of the foremost researchers of 2017,” said Hébert. “His work has shone an international spotlight on Montreal, which is rapidly becoming the place to be in the world of artificial intelligence.”
She added, “Yoshua Bengio is also an inspiration to me as a researcher, because he shows a genuine concern for the social and ethical issues raised by his work and that of his colleagues. He doesn’t hesitate to participate in the public debate.”
Hébert pointed out that Professor Bengio also contributed to the creation of the Montreal Declaration for a Responsible Development of Artificial Intelligence, currently being drafted at UdeM.
A world pioneer in AI
In addition to his work at Université de Montréal and the Department of Computer Science and Operations Research, Bengio is scientific director of the Institute for Data Valorization, co-director of the program on Learning in Machines & Brains of the Canadian Institute for Advanced Research, and Canada Research Chair in Statistical Learning Algorithms.
A leading world authority on deep learning, Professor Bengio’s goal is to understand the mathematical and computational mechanisms that give rise to intelligence through learning. Among his many contributions, he is widely recognized for his theoretical results on recurrent neural networks, kernel machines, distributed representations, depth of neural architectures and the optimization challenge of deep learning.
His work has been crucial in advancing knowledge on how deep networks are trained, how neural networks can learn vector embeddings for words, how to perform automatic translation with deep learning by taking advantage of an attention mechanism and how to perform unsupervised learning with deep generative models.