Portrait of Yoshua Bengio

Yoshua Bengio

Core Academic Member
Canada CIFAR AI Chair
Full Professor, Université de Montréal, Department of Computer Science and Operations Research Department
Scientific Director, Leadership Team
Observer, Board of Directors, Mila

Biography

*For media requests, please write to medias@mila.quebec

Yoshua Bengio is recognized worldwide as a leading expert in AI. He is most known for his pioneering work in deep learning, which earned him the 2018 A.M. Turing Award, “the Nobel Prize of computing,” with Geoffrey Hinton and Yann LeCun.

Bengio is a full professor at Université de Montréal, and the founder and scientific director of Mila – Quebec Artificial Intelligence Institute. He is also a senior fellow at CIFAR and co-directs its Learning in Machines & Brains program, serves as scientific director of IVADO, and holds a Canada CIFAR AI Chair.

In 2019, Bengio was awarded the prestigious Killam Prize and in 2022, he was the most cited computer scientist in the world by h-index. He is a Fellow of the Royal Society of London, Fellow of the Royal Society of Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. In 2023, he was appointed to the UN’s Scientific Advisory Board for Independent Advice on Breakthroughs in Science and Technology.

Concerned about the social impact of AI, Bengio helped draft the Montréal Declaration for the Responsible Development of Artificial Intelligence and continues to raise awareness about the importance of mitigating the potentially catastrophic risks associated with future AI systems.

For more information please contact Julie Mongeau, executive assistant.

Current Students

Mohammed Abukalam
Research Intern - Université de Montréal
mohammed.abukalam@mila.quebec
Rim Assouel
PhD - Université de Montréal
assouelr@mila.quebec
Dan Assouline
Collaborating Alumni
dan.assouline@mila.quebec
Ayoub Atanane
Research Intern - Université du Québec à Rimouski
ayoub.atanane@mila.quebec
Aayush Bajaj
Professional Master's - Université de Montréal
Co-supervisor :
aayush.bajaj@mila.quebec
Stefan Bauer
Visiting Researcher
Co-supervisor :
stefan.bauer@mila.quebec
Loubna Benabbou
Visiting Researcher - UQAR
loubna.benabbou@mila.quebec
Paul Bertin
PhD - Université de Montréal
bertinpa@mila.quebec
Boukachab Boukachab
Research Intern - UQAR
ghait.boukachab@mila.quebec
Oussama Boussif
PhD - Université de Montréal
oussama.boussif@mila.quebec
Andrés Campero
Visiting Researcher - MIT
andres.campero@mila.quebec
Chen Chen
Postdoctorate - Université de Montréal
Co-supervisor :
chen.sun@mila.quebec
Xiaoyin Chen
PhD - Université de Montréal
xiaoyin.chen@mila.quebec
Lancelot Da Costa
Research Intern - Imperial College
lancelot.dacosta@mila.quebec
Aman Dalmia
Professional Master's - Université de Montréal
aman.dalmia@mila.quebec
Subhrajyoti Dasgupta
Professional Master's - Université de Montréal
subhrajyoti.dasgupta@mila.quebec
Pierre-Paul De Breuck
Collaborating Alumni - Université de Montréal
pierre-paul.de-breuck@mila.quebec
Tristan Deleu
PhD - Université de Montréal
deleutri@mila.quebec
Aniket Didolkar
PhD - Université de Montréal
aniket.didolkar@mila.quebec
Alexandre Duval
Collaborating Researcher - Université Paris-Saclay
Principal supervisor :
alexandre.duval@mila.quebec
Eric Elmoznino
PhD - Université de Montréal
Co-supervisor :
eric.elmoznino@mila.quebec
Akram Erraqabi
PhD - Université de Montréal
akram.erraqabi@mila.quebec
Katie Everett
PhD - Massachusetts Institute of Technology
katie-elizabeth.everett@mila.quebec
Léna Ezzine
PhD - Université de Montréal
lena-nehale.ezzine@mila.quebec
Jean-pierre Falet
PhD - Université de Montréal
Co-supervisor :
jean-pierre.falet@mila.quebec
Leo Feng
PhD - Université de Montréal
leo.feng@mila.quebec
Jerome Francis
Professional Master's - Université de Montréal
jerome.francis@mila.quebec
Ahmad Ghawanmeh
Professional Master's - Université de Montréal
ahmad.ghawanmeh@mila.quebec
Clemence Granade
Professional Master's - Université de Montréal
clemence.granade@mila.quebec
Pietro Greiner
Collaborating Researcher - Université de Montréal
pietro.greiner@mila.quebec
Mohsin Hasan
PhD - Université de Montréal
mohsin.hasan@mila.quebec
Alejandro Hernández Garcia
Postdoctorate - Université de Montréal
Co-supervisor :
hernanga@mila.quebec
Leon Hetzel
Visiting Researcher - Technical University Munich (TUM)
leon.hetzel@mila.quebec
Edward Hu
PhD - Université de Montréal
edward.hu@mila.quebec
Moksh Jain
PhD - Université de Montréal
moksh.jain@mila.quebec
George Jiangyan Ma
Research Intern - Université de Montréal
jiangyan.ma@mila.quebec
Thomas Jiralerspong
Master's Research - Université de Montréal
Co-supervisor :
thomas.jiralerspong@mila.quebec
Younesse Kaddar
Research Intern - Université de Montréal
younesse.kaddar@mila.quebec
Minsu Kim
Research Intern - Université de Montréal
minsu.kim@mila.quebec
Maksym Korablyov
PhD - Université de Montréal
korablym@mila.quebec
Michał Koziarski
Postdoctorate - Université de Montréal
michal.koziarski@mila.quebec
Salem Lahlou
PhD - Université de Montréal
lahlosal@mila.quebec
Hae-Beom Lee
Collaborating Alumni
hae-beom.lee@mila.quebec
Seanie Lee
Research Intern - Université de Montréal
seanie.lee@mila.quebec
Mingze Li
Professional Master's - Université de Montréal
mingze2.li@mila.quebec
Chenghao Liu
Collaborating Alumni
liucheng@mila.quebec
Zhen Liu
PhD - Université de Montréal
Principal supervisor :
liuzhen@mila.quebec
Stephen Lu
Research Intern - McGill University
stephen.lu@mila.quebec
Kanika Madan
PhD - Université de Montréal
madankan@mila.quebec
Mohammed Mahfoud
Research Intern - Université de Montréal
mohammed.mahfoud@mila.quebec
Nikolay Malkin
Collaborating Alumni - Université de Montréal
nikolay.malkin@mila.quebec
Loic Mandine
DESS - Université de Montréal
loic.mandine@mila.quebec
Cristian Dragos Manta
PhD - Université de Montréal
Co-supervisor :
cristian-dragos.manta@mila.quebec
Stefano Massaroli
Postdoctorate - Université de Montréal
stefano.massaroli@mila.quebec
Cristian Meo
Collaborating Alumni
cristian.meo@mila.quebec
Sören Mindermann
Postdoctorate - Université de Montréal
soren.mindermann@mila.quebec
Sarthak Mittal
PhD - Université de Montréal
Principal supervisor :
mittalsa@mila.quebec
Jama Mohamud
PhD - Université de Montréal
Principal supervisor :
hussein-mohamu.jama@mila.quebec
Nama Venkatesh Nama Venkatesh
Professional Master's - Université de Montréal
priya.nama@mila.quebec
Brady Neal
PhD - Université de Montréal
Principal supervisor :
nealbray@mila.quebec
Phong Nguyen
Visiting Researcher - Université de Montréal
nguyenph@mila.quebec
Yashaswi Pupneja
Professional Master's - Université de Montréal
yashaswi.pupneja@mila.quebec
Vincent Quirion
Undergraduate - Université de Montréal
vincent.quirion@mila.quebec
Nassim Rahaman
PhD - Max-Planck-Institute for Intelligent Systems
rahamann@mila.quebec
Param Raval
Professional Master's - Université de Montréal
param.raval@mila.quebec
Jarrid Rector-Brooks
PhD - Université de Montréal
jarrid.rector-brooks@mila.quebec
James Requeima
Visiting Researcher - Université de Montréal
james.requeima@mila.quebec
Camille Rochefort-Boulanger
PhD - Université de Montréal
Principal supervisor :
rochefoc@mila.quebec
Theo Saulus
Collaborating Researcher
Principal supervisor :
theo.saulus@mila.quebec
Victor Schmidt
PhD - Université de Montréal
schmidtv@mila.quebec
Luca Scimeca
Postdoctorate - Université de Montréal
luca.scimeca@mila.quebec
Dragos Secrieru
Master's Research - Université de Montréal
dragos.secrieru@mila.quebec
Marcin/Martin Sendera
Research Intern - Université de Montréal
marcin.sendera@mila.quebec
Vedant Shah
Master's Research - Université de Montréal
vedant.shah@mila.quebec
Zibo Shang
Professional Master's - Université de Montréal
zibo.shang@mila.quebec
Divya Sharma
Collaborating Alumni
divya.sharma@mila.quebec
Anja Surina
PhD - École Polytechnique Montréal Fédérale de Lausanne
anja.surina@mila.quebec
Mélisande Astrid Crystal Teng
PhD - Université de Montréal
Co-supervisor :
tengmeli@mila.quebec
Basile Terver
Collaborating Researcher
Principal supervisor :
basile.terver@mila.quebec
Alexander Tong
Postdoctorate - Université de Montréal
alexander.tong@mila.quebec
Prudencio Tossou
Collaborating Researcher - Valence
Principal supervisor :
prudencio.tossou@mila.quebec
Donna Vakalis
Postdoctorate - Université de Montréal
Co-supervisor :
donna.vakalis@mila.quebec
Sasha Volokhova
PhD - Université de Montréal
alexandra.volokhova@mila.quebec
Yizhao Wang
Professional Master's - Université de Montréal
yizhao.wang@mila.quebec
Zichao Yan
Postdoctorate - Université de Montréal
yanzicha@mila.quebec
Nicole Zhang
PhD - McGill University
Principal supervisor :
xi.zhang@mila.quebec
Dinghuai Zhang
PhD - Université de Montréal
Principal supervisor :
dinghuai.zhang@mila.quebec
Ruixiang Zhang
PhD - Université de Montréal
Principal supervisor :
zhangrui@mila.quebec
William Zhang
PhD - Université de Montréal
tianyu.zhang@mila.quebec
Harry Zhao
PhD - McGill University
Principal supervisor :
zhaoming@mila.quebec

Publications

Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Usman Anwar
Abulhair Saparov
Javier Rando
Daniel Paleka
Miles Turpin
Peter Hase
Ekdeep Singh Lubana
Erik Jenner
Stephen Casper
Oliver Sourbut
Benjamin L. Edelman
Zhaowei Zhang
Mario Gunther
Anton Korinek
Jose Hernandez-Orallo
Lewis Hammond
Eric J Bigelow
Alexander Pan
Lauro Langosco
Tomasz Korbak … (see 18 more)
Heidi Zhang
Ruiqi Zhong
Sean 'o H'eigeartaigh
Gabriel Recchia
Giulio Corsi
Alan Chan
Markus Anderljung
Lilian Edwards
Danqi Chen
Samuel Albanie
Jakob Foerster
Florian Tramer
He He
Atoosa Kasirzadeh
Yejin Choi
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are o… (see more)rganized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose
Government Interventions to Avert Future Catastrophic AI Risks
Regulating advanced artificial agents
Michael K. Cohen
Noam Kolt
Gillian K. Hadfield
Stuart Russell
Language Models Can Reduce Asymmetry in Information Markets
Nasim Rahaman
Martin Weiss
Manuel Wüthrich
Erran L. Li
Bernhard Schölkopf
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Minsu Kim
Sanghyeok Choi
Jiwoo Son
Hyeon-Seob Kim
Jinkyoo Park
Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport
Alexander Tong
Nikolay Malkin
Guillaume Huguet
Yanlei Zhang
Jarrid Rector-Brooks
Kilian FATRAS
Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their si… (see more)mulation-based maximum likelihood training. We introduce the generalized \textit{conditional flow matching} (CFM) technique, a family of simulation-free training objectives for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, CFM does not require the source distribution to be Gaussian or require evaluation of its density. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Furthermore, OT-CFM is the first method to compute dynamic OT in a simulation-free way. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schrödinger bridge inference.
Integrating Generative and Experimental Platforms or Biomolecular Design
Cheng-Hao Liu
Jarrid Rector-Brooks
Jason Yim
Soojung Yang
Sidney Lisanza
Francesca-Zhoufan Li
Pranam Chatterjee
Tommi Jaakkola
Regina Barzilay
David Baker
Frances H. Arnold
Tackling Climate Change with Machine Learning: Fostering the Maturity of ML Applications for Climate Change
Shiva Madadkhani
Olivia Mendivil Ramos
Millie Chapman
Jesse Dunietz
Arthur Ouaknine
Machine learning and information theory concepts towards an AI Mathematician
Nikolay Malkin
The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms … (see more)of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities -- which correspond to our intuition and habitual behaviors -- but still lacks something important regarding system 2 abilities -- which include reasoning and robust uncertainty estimation. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement, which could guide future work in crafting an AI mathematician. The focus is not on proving a given theorem but on discovering new and interesting conjectures. The central hypothesis is that a desirable body of theorems better summarizes the set of all provable statements, for example by having a small description length while at the same time being close (in terms of number of derivation steps) to many provable statements.
Towards DNA-Encoded Library Generation with GFlowNets
Michał Koziarski
Mohammed Abukalam
Vedant Shah
Louis Vaillancourt
Doris Alexandra Schuetz
Moksh J. Jain
Almer M. van der Sloot
Mathieu Bourgey
Anne Marinier
Sources of richness and ineffability for phenomenally conscious states
Xu Ji
Eric Elmoznino
George Deane
Axel Constant
Jonathan Simon
Distributional GFlowNets with Quantile Flows
Dinghuai Zhang
Ling Pan
Ricky T. Q. Chen
Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an agent learns a stochastic policy for generating com… (see more)plex combinatorial structure through a series of decision-making steps. Despite being inspired from reinforcement learning, the current GFlowNet framework is relatively limited in its applicability and cannot handle stochasticity in the reward function. In this work, we adopt a distributional paradigm for GFlowNets, turning each flow function into a distribution, thus providing more informative learning signals during training. By parameterizing each edge flow through their quantile functions, our proposed \textit{quantile matching} GFlowNet learning algorithm is able to learn a risk-sensitive policy, an essential component for handling scenarios with risk uncertainty. Moreover, we find that the distributional approach can achieve substantial improvement on existing benchmarks compared to prior methods due to our enhanced training algorithm, even in settings with deterministic rewards.