AI4 Science

One of the most promising perspectives of machine learning algorithms is their ability to quickly collate and analyze large amounts of data. Artificial intelligence (AI) can help push the boundaries of scientific research with its ability to assess decades worth of data.

A researcher adding drops of liquid to a test tube using a micropipette.

Scientific fields such as biology, genomics, chemistry, climate science, and physics have all accumulated quantities of data so vast that it would take many human lifetimes to read and digest the information.

Using machine learning, scientists can now tackle complex and multidisciplinary challenges by accelerating the analysis of data, discovering new connections, simulating and proposing experiments, and developing new theories.

Featured Projects

Lab equipment in a scientific laboratory.

Enhancing Super-Resolution Microscopy with AI

This project aims to further develop AI-assisted SRM microscopy approaches to improve its performance in living cells and tissues, diversify the applications of SRM microscopy in neuroscience, and more.

Learn more

AI is an all-encompassing technology that can help us move beyond bottlenecks and speed up research in many scientific areas to help humanity tackle our most pressing challenges.

Yoshua Bengio, Full Professor, Université de Montréal, Founder and Scientific Director, Mila

Resources

IVADO Strategic Research Funding Program

This research framework program has been developed in collaboration by research teams to address major scientific challenges in digital intelligence.

Learn more https://ivado.ca/en/scholarships-and-grants/strategic-framework-research-progra…

Research Labs

Mila professors exploring the subject as part of their research.

Mila Faculty

Associate Academic Member

Ian Arawjo

Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research

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Associate Academic Member

Narges Armanfard

Assistant Professor, McGill University, Department of Electrical and Computer Engineering

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Associate Academic Member

Shahab Bakhtiari

Assistant Professor, Université de Montréal, Department of Psychology

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Affiliate Member

Giovanni Beltrame

Full Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering

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Core Academic Member

Yoshua Bengio

Full Professor, Université de Montréal, Department of Computer Science and Operations Research Department

Canada CIFAR AI Chair

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Core Academic Member

Glen Berseth

Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research

Canada CIFAR AI Chair

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Associate Academic Member

Marco Bonizzato

Assistant Professor, Polytechnique Montréal, Department of Electrical Engineering

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Core Industry Member

Pablo Samuel Castro

Research Software Developer, Google

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Core Academic Member

Sarath Chandar Anbil Parthipan

Assistant Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering

Canada CIFAR AI Chair

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Associate Academic Member

Ian Charest

Assistant Professor, Université de Montréal, Department of Psychology

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Affiliate Member

Jun Ding

Assistant professor, McGill University, Department of Medicine

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Associate Industry Member

Alexandre Drouin

Research Scientist, ServiceNow

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Associate Academic Member

Audrey Durand

Assistant Professor, Université Laval, Department of Computer Science and Software Engineering

Canada CIFAR AI Chair

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Associate Academic Member

Amin Emad

Assistant Professor, McGill University, Department of Electrical and Computer Engineering

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Associate Academic Member

Paul François

Full Professor, Université de Montréal, Department of Biochemistry and Molecular Medicine

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Core Industry Member

Marc Gendron-Bellemare

Chief Scientific Officer, Reliant AI

Canada CIFAR AI Chair

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Core Academic Member

Gauthier Gidel

Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research

Canada CIFAR AI Chair

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Associate Academic Member

Jin Guo

Assistant Professor, McGill University, School of Computer Science

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Associate Academic Member

Yashar Hezaveh

Assistant Professor, Université de Montréal, Department of Physics

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Associate Academic Member

Karim Jerbi

Associate Professor, Université de Montréal, Department of Psychology

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Associate Academic Member

Eric Kolaczyk

Professor, McGill University, Department of Mathematics and Statistics

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Core Academic Member

Simon Lacoste-Julien

Associate Scientific Director, Mila, Associate Professor, Université de Montréal, Department of Computer Science and Operations Research

Canada CIFAR AI Chair

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Associate Academic Member

Sébastien Lemieux

Associate Professor, Université de Montréal, Department of Computer Science and Operations Research and Department of Biochemistry and Molecular Medicine

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Associate Academic Member

Bang Liu

Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research

Canada CIFAR AI Chair

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Associate Academic Member

Xue (Steve) Liu

Full Professor, McGill University, School of Computer Science

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Affiliate Member

Tegan Maharaj

Tenured Professor, University of Toronto

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Core Academic Member

Chengzhi Mao

Assistant Professor, McGill University, Department of Electrical and Computer Engineering

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Associate Academic Member

Eilif Benjamin Muller

Assistant Professor, Université de Montréal, Department of Neurosciences

Canada CIFAR AI Chair

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Core Academic Member

Derek Nowrouzezahrai

Associate Professor, McGill University, Department of Electrical and Computer Engineering

Canada CIFAR AI Chair

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Core Academic Member

Chris Pal

Full Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering

Canada CIFAR AI Chair

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Core Academic Member

Doina Precup

Associate Professor, McGill University, School of Computer Science

Canada CIFAR AI Chair

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Core Academic Member

Reihaneh Rabbany

Assistant Professor, McGill University, School of Computer Science

Canada CIFAR AI Chair

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Core Academic Member

Guillaume Rabusseau

Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research

Canada CIFAR AI Chair

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Core Academic Member

Siamak Ravanbakhsh

Assistant Professor, McGill University, School of Computer Science

Canada CIFAR AI Chair

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Core Academic Member

Blake Richards

Associate Professor, McGill University, School of Computer Science and Department of Neurology and Neurosurgery

Canada CIFAR AI Chair

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Core Academic Member

Irina Rish

Full Professor, Université de Montréal, Department of Computer Science and Operations Research Department

Canada CIFAR AI Chair

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Core Academic Member

David Rolnick

Assistant Professor, McGill University, School of Computer Science

Canada CIFAR AI Chair

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Associate Academic Member

Kaleem Siddiqi

Professor, McGill University, School of Computer Science

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Core Academic Member

Guy Wolf

Associate Professor, Université de Montréal, Department of Mathematics and Statistics

Canada CIFAR AI Chair

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Associate Academic Member

Archer Yang

Associate professor, McGill University, Department of Mathematics and Statistics

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Publications

Scientific discovery in the age of artificial intelligence

Hanchen Wang

Tianfan Fu

Yuanqi Du

Wenhao Gao

Kexin Huang

Ziming Liu

Payal Chandak

Shengchao Liu

Peter Van Katwyk

Andreea Deac

Animashree Anandkumar

K. Bergen

Carla P. Gomes

Shirley Ho

Pushmeet Kohli

Joan Lasenby

Jure Leskovec

Tie-Yan Liu

A. Manrai

Debora Susan Marks … (see 10 more)

Bharath Ramsundar

Le Song

Jimeng Sun

Jian Tang

Petar Veličković

Max Welling

Linfeng Zhang

Connor Wilson. Coley

Yoshua Bengio

Marinka Žitnik

2023-08-01

Nature (published)

doi.org

Neural Causal Structure Discovery from Interventions

Nan Rosemary Ke

Olexa Bilaniuk

Anirudh Goyal

Stefan Bauer

Hugo Larochelle

Bernhard Schölkopf

Michael Curtis Mozer

Chris Pal

Yoshua Bengio

Recent promising results have generated a surge of interest in continuous optimization methods for causal discovery from observational data.… (see more) However, there are theoretical limitations on the identifiability of underlying structures obtained solely from observational data. Interventional data, on the other hand, provides richer information about the underlying data-generating process. Nevertheless, extending and applying methods designed for observational data to include interventions is a challenging problem. To address this issue, we propose a general framework based on neural networks to develop models that incorporate both observational and interventional data. Notably, our method can handle the challenging and realistic scenario where the identity of the intervened upon variable is unknown. We evaluate our proposed approach in the context of graph recovery, both de novo and from a partially-known edge set. Our method achieves strong benchmark results on various structure learning tasks, including structure recovery of synthetic graphs as well as standard graphs from the Bayesian Network Repository.

2023-09-10

TMLR (accepted)

openreview.net

Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles

Yao Lu

Yue Dong

Laurent Charlin

Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-sc… (see more)ale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. Our work is inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and empirical results—using several state-of-the-art models trained on the Multi-XScience dataset—reveal that Multi-XScience is well suited for abstractive models.

2020-11-01

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (published)

doi.org

arxiv.org

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