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Inspiring the development of artificial intelligence for the benefit of all 

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Located in the heart of Quebec’s AI ecosystem, Mila is a community of more than 1,200 researchers specializing in machine learning and dedicated to scientific excellence and innovation.

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News

04 Jun 2025
Mila and Mastercard announce collaboration to improve AI fairness and transparency
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02 Jun 2025
Mila Welcomes Second Cohort of Indigenous Pathfinders in AI
Visual identity of the Indigenous Pathfinders in AI program.
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22 May 2025
Alfred partners with Mila to accelerate AI innovation in the food and beverage sector
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Faculty 

Founded in 1993 by Professor Yoshua Bengio, Mila today brings together over 140 professors affiliated with Université de Montréal, McGill University, Polytechnique Montréal and HEC Montréal. Mila also welcomes professors from Université Laval, Université de Sherbrooke, École de technologie supérieure (ÉTS) and Concordia University. 

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Latest Publications

Adaptation, Comparison and Practical Implementation of Fairness Schemes in Kidney Exchange Programs
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
In Kidney Exchange Programs (KEPs), each participating patient is registered together with an incompatible donor. Donors without an incompat… (see more)ible patient can also register. Then, KEPs typically maximize overall patient benefit through donor exchanges. This aggregation of benefits calls into question potential individual patient disparities in terms of access to transplantation in KEPs. Considering solely this utilitarian objective may become an issue in the case where multiple exchange plans are optimal or near-optimal. In fact, current KEP policies are all-or-nothing, meaning that only one exchange plan is determined. Each patient is either selected or not as part of that unique solution. In this work, we seek instead to find a policy that contemplates the probability of patients of being in a solution. To guide the determination of our policy, we adapt popular fairness schemes to KEPs to balance the usual approach of maximizing the utilitarian objective. Different combinations of fairness and utilitarian objectives are modelled as conic programs with an exponential number of variables. We propose a column generation approach to solve them effectively in practice. Finally, we make an extensive comparison of the different schemes in terms of the balance of utility and fairness score, and validate the scalability of our methodology for benchmark instances from the literature.
2025-08-01
European Journal of Operational Research (published)
doi.org
arxiv.org
Clarifying a working definition for ‘precision communication’: a scoping review of medical literature on communication
Bao-Lam Pham
Brigitte N. Durieux
Amanda Bianco
Corinne Cécyre-Chartrand
Elena Guadagno
Amalia M. Issa
Dan Poenaru
2025-06-05
Personalized Medicine (published)
doi.org
Policy context and digital development: a comparative study of trajectories in 4 Canadian academic health centers over 30 years
Aude Motulsky
Susan Usher
Pascale Lehoux
Catherine Régis
Trish Reay
Paul Hebert
Lise Gauvin
Alain Biron
G Ross Baker
Marie-Pierre Moreault
Johanne Préval
Jean-Louis Denis
2025-06-05
Journal of the American Medical Informatics Association (published)
doi.org
Galaxy cluster characterization with machine learning techniques
M. Sadikov
J. Hlavacek-Larrondo
Laurence Perreault-Levasseur
C. L. Rhea
M. McDonald
M. Ntampaka
J. ZuHone
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizi… (see more)ng machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.
2025-06-03
The Astrophysical Journal (published)
doi.org
arxiv.org
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AI for Humanity

Socially responsible and beneficial development of AI is a fundamental component of Mila’s mission. As a leader in the field, we wish to contribute to social dialogue and the development of applications that will benefit society.

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