Yoshua Bengio

A.M. Turing Award
Full Professor, Department of Computer Science and Operations Research, Université de Montréal.
Canada Research Chair in Statistical Learning Algorithms.
Founder and Scientific Director of Mila.
Scientific Director of IVADO. CIFAR Fellow and Program Director.

Yoshua Bengio is recognized as one of the world’s leading experts in artificial intelligence and a pioneer in deep learning.

Since 1993, he has been a professor in the Department of Computer Science and Operational Research at the Université de Montréal. In addition to holding the Canada Research Chair in Statistical Learning Algorithms, he is the founder and scientific director of Mila, the Quebec Institute of Artificial Intelligence, the world’s largest university-based research group in deep learning.

His contribution to research is undeniable. In 2018, Yoshua Bengio is the computer scientist who collected the largest number of new citations in the world, thanks to his many publications.

Long version

Yoshua Bengio is recognized as one of the world’s leading experts in artificial intelligence and a pioneer in deep learning. Following his studies in Montreal, culminating in a Ph.D. in computer science from McGill University in 1991, Professor Bengio did postdoctoral studies at the Massachusetts Institute of Technology (MIT) in Boston.

Since 1993, Yoshua Bengio has been a professor in the Department of Computer Science and Operational Research at the Université de Montréal. In addition to holding the Canada Research Chair in Statistical Learning Algorithms, he is also the founder and scientific director of Mila, the Quebec Institute of Artificial Intelligence, the world’s largest university-based research group in deep learning. He is also the Scientific Director of IVADO.

His contribution to research is undeniable. In 2018, Yoshua Bengio is the computer scientist who collected the largest number of new citations in the world, thanks to his three books and some 500 publications.

His ultimate goal is to understand the principles that lead to intelligence through learning and his research has earned him multiple awards. In 2017, he was made an Officer of the Order of Canada and a Fellow of the Royal Society of Canada, in addition to receiving the Prix Marie-Victorin and being named Scientist of the Year by Radio-Canada. In 2018, he was also awarded the 50th Anniversary Medal by Quebec’s Ministère des Relations internationales et de la Francophonie. In 2019, he was awarded the Killam Prize as well as the 2018 Turing Award, considered to be the Nobel prize for computing. These honours reflect the profound influence of his work on the evolution of our society.

Concerned about the social impacts of this new technology, he actively contributed to the development of the Montreal Declaration for Responsible Development of Artificial Intelligence.

Selected publications

Goodfellow, Ian J., Yoshua Bengio, and Aaron Courville (2016). Deep Learning. MIT Press. 

Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio (2015). “Neural Machine Translation by Jointly Learning to Align and Translate”. In: ICLR’2015, arXiv:1409.0473

LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton (2015). “Deep Learning”. In: Nature 521.7553, pp. 436–444.

Dauphin, Yann, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio (2014). “Identifying and attacking the saddle point problem in high-dimensional non-convex optimization”. In: NIPS’2014, arXiv:1406.2572.

Goodfellow, Ian J., Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio (2014). “Generative Adversarial Networks”. In: NIPS’2014, arXiv:1406.2661.

Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent (2013). “Generalized Denoising Auto-Encoders as Generative Models”. In: NIPS’2013, arXiv:1305.6663.

Pascanu, Razvan, Guido Montufar, and Yoshua Bengio (2014). “On the number of inference regions of deep feed forward networks with piece-wise linear activations”. In: International Conference on Learning Representations 2014 (Conference Track),arXiv:1305.6663.

Glorot, Xavier and Yoshua Bengio (2010). “Understanding the difficulty of training deep feedforward neural networks”. In: AISTATS’2010.

Bengio, Yoshua, Jerome Louradour, Ronan Collobert, and Jason Weston (2009). “Curriculum Learning”. In: ICML’09, 2009_curriculum_icml.

Bengio, Yoshua (2009a). “Learning deep architectures for AI”. In: Foundations and Trends in Machine Learning 2.1, pp. 1–127.

Bengio, Yoshua, Pascal Lamblin, Dan Popovici, and Hugo Larochelle (2007). “Greedy Layer-Wise Training of Deep Networks”. In: NIPS’2006. Ed. by Bernhard Schölkopf, John Platt, and Thomas Hoffman. MIT Press, pp. 153–160.

Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol (2008). “Ex-tracting and Composing Robust Features with Denoising Autoencoders”. In: Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML’08). Ed. by William W. Cohen, Andrew McCallum, and Sam T. Roweis. ACM, pp. 1096–1103.

Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin (2003). “A Neural Probabilistic Language Model”. In: Journal of Machine Learning Research 3, pp. 1137– 1155.

Bengio, Yoshua and Samy Bengio (2000). “Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks”. In: Advances in Neural Information Processing Systems 12 (NIPS’99). Ed. by S.A. Solla, T.K. Leen, and K-R. Müller. MIT Press, pp. 400–406.

LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick Haffner (1998). “Gradient-Based Learning Applied to Document Recognition”. In: Proceedings of the IEEE 86.11, pp. 2278– 2324.

Bengio, Y., P. Simard, and P. Frasconi (1994). “Learning Long-Term Dependencies with Gradient Descent is Difficult”. In: IEEE Transactions on Neural Networks 5.2, pp. 157– 166.

Bengio, Yoshua, Samy Bengio, Jocelyn Cloutier, and Jan Gecsei (1991). “Learning a Synaptic Learning Rule”. In: International Joint Conference on Neural Networks (IJCNN). Seattle, WA, II–A969. 

Education


1992 – 1993
Post-doctoral Fellow, AT&T Bell Laboratories, NJ, USA.
Learning and Vision Algorithms​ | ​Larry Jackel and Yann LeCun’s group

1991 – 1992
Post-doctoral Fellow, MIT, MA, USA.
NSERC scholarship | Statistical Learning / Sequential Data
Department of Brain and Cognitive Sciences | Michael I. Jordan's Group

1988 – 1991
Ph.D. in Computer Science, McGill University, Montreal
NSERC scholarship | Neural Networks and Markovian Models

1986 – 1988
M.Sc. in Computer Science, McGill University, Montreal
Speech Recognition with Statistical Methods

1982 – 1986
B.Eng. in Computer Engineering, Honours, McGill University, Montreal

Titles and Distinctions

2018 – 2023
Chair, CIFAR AI (CCAI) ($1.25M)

2018 – present
Founder and Scientific Director, Mila, the Quebec AI Institute

2016 – present
Scientific Director, IVADO, the Data Valorization Institute

2019 – present
Co-Chair of the AI Advisory Council, government of Canada

2016-2023
Recipient of CFREF Grant ($93.6M), 2016 - 2023
Leading applicant for Data Serving Canadians: Deep Learning and Optimization for the Knowledge Revolution; the largest grant ever received at U.Montreal.

2014 – present
Co-director, CIFAR LMB (Learning in Machines in Brains) program
Previously called NCAP and originally led by Geoff Hinton, this program funded the initial breakthroughs in deep learning.

2013 – present
Creator and General Chair, ICLR (International Conference on Learning Representations)

2012 – 2013
Awards Committee, Canadian Association for Computer Science
Also member of the NeurIPS 2012 and ICML 2016 committees for best paper awards, and NeurIPS committees for choosing the next program chairs 2013-2018.

2010 – present
Member of the board, Neural Information Processing Systems (NeurIPS) Foundation (Formerly NIPS)

2009
General Chair, NeurIPS
NeurIPS is a very high-level conference - the most important in the field (> 1000 submissions) - with reviewing and acceptance criteria comparing favorably to the best journals (acceptance rate between 20% and 25%). Having 56 papers published in NeurIPS over the years puts me among the most prolific contributors to the NeurIPS community.

2008
Program Co-Chair, NeurIPS 2008

2005 – 2015
Industrial Research Chair, NSERC, 2005 - 2015.

2004 – present
Senior Fellow, CIFAR (Canadian Institute For Advanced Research)

2000 – present
Canada Research Chair on Statistical Learning Algorithms
Tier 2, 2000-2005 ; Tier 1, 2006 – present.

1999 – 2009
Member of the board, the Centre de Recherches Mathématiques (UdeM)

1993
Founder and Scientific Director, Mila - Quebec Artificial Intelligence Institute
Formerly the LISA (founded 1993), Mila brings together the researchers of Université de Montréal and McGill University in an independent non-profit organization. With 300 researchers, including 15 faculty, it is the largest academic center for deep learning research in the world, yielding pioneering papers in the field, including the introduction of deep learning (2006), curriculum learning (2009), showing the power of ReLUs for deeper nets (2011), and the breakthroughs brought by GANs and neural machine translation (2014).

Prizes and Awards

2019
A. M. Turing Award, Association for Computing Machinery, awarded jointly with Geoffrey Hinton and Yann LeCun for 2018

2019
Killam Prize for natural sciences, Canada Council for the Arts

2019
IEEE CIS Neural Networks Pioneer Award, IEEE Computational Intelligence Society

2018
Lifetime Achievement Award, Canadian AI Association

2018
Medal of the 50th Anniversary of the Ministry of International Relations and Francophonie

2017
Marie-Victorin Quebec Prize
Highest distinction in the sciences for the province of Québec

2017
Radio-Canada’s Scientist of the Year

2017
Member of the Royal Society of Canada

2017
Officer of the Order of Canada

2015
La Recherche 10 Discoveries That Changed Science 2015
For work on neural networks local minima.

2009
ACFAS Urgel-Archambault Prize