Profile - Yoshua Bengio

Main titles and distinctions

A.M. Turing Award 2018
Killam Prize in Natural Sciences 2018
Fellow of the Royal Society of London 2020
Fellow of the Royal Society of Canada 2017
Knight of the Legion of Honor of France 2022
Officer of the Order of Canada 2017
IEEE CIS Neural Networks Pioneer award 2019
Full Professor, Department of Computer Science and Operations Research, UdeM
Founder and Scientific Director of Mila
Scientific Director of IVADO
Canada CIFAR AI Chair
Co-Director of the CIFAR Learning in Machines & Brains program

Biography

Short Version:

Yoshua Bengio is Full Professor in the Department of Computer Science and Operations Research at Université de Montreal, as well as the Founder and Scientific Director of Mila and the Scientific Director of IVADO. Considered one of the world’s leaders in artificial intelligence and deep learning, he is the recipient of the 2018 A.M. Turing Award with Geoff Hinton and Yann LeCun, known as the Nobel prize of computing.

He is a Canada CIFAR AI Chair, a Fellow of both the Royal Society of London and Canada, an Officer of the Order of Canada, Knight of the Legion of Honor of France, Member of the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology, and Chair of the International Scientific Report on the Safety of Advanced AI.

Medium Version:

Recognized worldwide as one of the leading experts in artificial intelligence, Yoshua Bengio is most known for his pioneering work in deep learning, earning him the 2018 A.M. Turing Award, “the Nobel Prize of Computing,” with Geoffrey Hinton and Yann LeCun.

He is Full Professor at Université de Montréal, and the Founder and Scientific Director of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow and acts as Scientific Director of IVADO.

In 2019, he was awarded the prestigious Killam Prize and for several years has been the computer scientist with the greatest impact in terms of citations, as measured by the h-index. He is a Fellow of both the Royal Society of London and Canada, Knight of the Legion of Honor of France, Officer of the Order of Canada, Member of the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology since 2023 and a Canada CIFAR AI Chair.

Concerned about the social impact of AI, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence and currently chairs the International Scientific Report on the Safety of Advanced AI.

Longer Version:

Yoshua Bengio is recognized worldwide as one of the leading experts in artificial intelligence, known for his conceptual and engineering breakthroughs in artificial neural networks and deep learning. He is a Full Professor in the Department of Computer Science and Operations Research at Université de Montréal and the Founder and Scientific Director of Mila – Quebec Artificial Intelligence Institute, one of the largest academic institutes in deep learning and one of the three federally-funded centers of excellence in AI research and innovation in Canada.

He began his studies in Montreal, where he obtained his Ph.D. in Computer Science from McGill University in 1991. After completing a postdoctoral fellowship at the Massachusetts Institute of Technology (MIT) on statistical learning and sequential data, he completed a second postdoc at AT&T Bell Laboratories, in Holmdel, NJ, on learning and vision algorithms in 1992-1993. In September 1993, he returned to Montreal and joined UdeM as a faculty member.

In 2016, he became the Scientific Director of IVADO. He is Co-Director of the CIFAR Learning in Machines & Brains program that funded the initial breakthroughs in deep learning and since 2019, holds a Canada CIFAR AI Chair and is Co-Chair of Canada’s Advisory Council on AI.

Since 2022, Yoshua Bengio has been the computer scientist with the greatest impact in terms of citations, as measured by the h-index. Concerned about the social impact of AI, he actively took part in the conception of the Montreal Declaration for the Responsible Development of Artificial Intelligence and currently chairs the International Scientific Report on the Safety of Advanced AI. His goal is to contribute to uncovering the principles giving rise to intelligence through learning while favouring the development of AI for the benefit of all.

Yoshua Bengio was made an Officer of the Order of Canada and a Fellow of the Royal Society of Canada in 2017 and in 2020, became a Fellow of the Royal Society of London. From 2000 to 2019, he held the Canada Research Chair in Statistical Learning Algorithms. He is a member of the NeurIPS Foundation advisory board and Co-Founder of the ICLR conference.

His scientific contributions have earned him numerous awards, including the 2019 Killam Prize for Natural Sciences, the 2017 Government of Québec Marie-Victorin Award, the 2018 Lifetime Achievement Award from the Canadian AI Association, the Prix d’excellence FRQNT (2019), the Medal of the 50th Anniversary of the Ministry of International Relations and Francophonie (2018), the 2019 IEEE CIS Neural Networks Pioneer Award, Acfas’s Urgel-Archambault Prize (2009) and in 2017, he was named Radio-Canada’s Scientist of the Year.

He is the 2018 laureate of the A.M. Turing Award, “the Nobel Prize of Computing,” alongside Geoffrey Hinton and Yann LeCun for their important contributions and advances in deep learning. In 2022, he was appointed Knight of the Legion of Honor by France and named co-laureate of Spain’s Princess of Asturias Award for technical and scientific research. In 2023, Yoshua Bengio was appointed a Member of the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology.

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.

Montufar, Guido F., Razvan Pascanu, KyungHyun Cho, and Yoshua Bengio (2014). “On the Number of Linear Regions of Deep Neural Networks”. In: NIPS’2014, arXiv:1402.1869.

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.

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.

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

Glorot, X., A. Bordes, and Y. Bengio (2011). “Deep Sparse Rectifier Neural Networks”. In: AISTATS’2011

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

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.

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.

Bengio, Y., Delalleau, O., Le Roux, N. (2005) “The Curse of Highly Variable Functions for Local Kernel Machines”. NIPS’2005

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 (full list)

2019 – present	Co-Chair of the AI Advisory Council, government of Canada
2018 – present	Co-founder and Scientific Director, Mila, the Quebec AI Institute
2018 – 2023	Chair, CIFAR AI (CCAI) ($1.25M)
2016 – present	Scientific Director, IVADO, the Data Valorization Institute
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 breakthrough in deep learning.
2013 – present	co-founder and member of the board of 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 advisory 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 – 2019	Canada Research Chair on Statistical Learning Algorithms Tier 2, 2000-2005 ; Tier 1, 2006 – 2019
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 2018, Association for Computing Machinery, awarded jointly with Geoffrey Hinton and Yann LeCun
2019	Killam Prize for natural sciences, Canada Council for the Arts
2019	IEEE CIS Neural Networks Pioneer Award, IEEE Computational Intelligence Society
2019	Prix d’excellence FRQNT, Fonds de recherche du Quebec – Nature et technologies
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