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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

27 Oct 2025
AI Researcher Yoshua Bengio Becomes First Living Scientist to Reach 1 Million Citations on Google Scholar
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21 Oct 2025
Aishwarya Agrawal awarded Everingham Prize 2025
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17 Oct 2025
BDC and Mila renew their partnership to support innovation and the competitiveness of Canadian SMEs with artificial intelligence
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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

The role of Large Language Models in IoT security: A systematic review of advances, challenges, and opportunities
Saeid Jamshidi
Negar Shahabi
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
Carol Fung
2025-11-01
Internet of Things (published)
doi.org
High IL1R1 expression predicts poor survival and benefit from stem cell transplant in intermediate-risk acute myeloid leukemia from the Leucegene cohort
Guillaume Richard-Carpentier
Francois Béliveau
Sandrine Lacoste
Banafsheh Khakipoor
Véronique Lisi
Michael Vladovsky
Miriam Marquis
Jean-Francois Spinella
Patrick Gendron
Sébastien Lemieux
Vincent-Philippe Lavallee
Guy Sauvageau
Josée Hébert
2025-10-23
Biomarker Research (published)
doi.org
The Intermodal Railroad Blocking and Railcar Fleet-Management Planning Problem
Julie Kienzle
Serge Bisaillon
T. Crainic
Emma Frejinger
Rail is a cost-effective and relatively low-emission mode for transporting intermodal containers over long distances. This paper addresses t… (see more)actical planning of intermodal railroad operations by introducing a new problem that simultaneously considers three consolidation processes and the management of a heterogeneous railcar fleet. We model the problem with a scheduled service network design with resource management (SSND-RM) formulation, expressed as an integer linear program. While such formulations are challenging to solve at scale, we demonstrate that our problem can be tackled with a general-purpose solver when provided with high-quality warm-start solutions. To this end, we design a construction heuristic inspired by a relax-and-fix procedure. We evaluate the methodology on realistic, large-scale instances from our industrial partner, the Canadian National Railway Company: a North American Class I railroad. The computational experiments show that the proposed approach efficiently solves practically relevant instances, and that solutions to the SSND-RM formulation yield substantially more accurate capacity estimations compared to those obtained from simpler baseline models. Managerial insights from our study highlight that ignoring railcar fleet management or container loading constraints can lead to a severe underestimation of required capacity, which may result in costly operational inefficiencies. Furthermore, our results show that the use of multi-platform railcars improves overall capacity utilization and benefits the network, even if they can locally lead to less efficient loading as measured by terminal-level slot utilization performance indicators.
2025-10-23
ArXiv (preprint)
arxiv.org
arxiv.org
A Derandomization Framework for Structure Discovery: Applications in Neural Networks and Beyond
Nikos Tsikouras
Yorgos Pantis
Ioannis Mitliagkas
Christos Tzamos
2025-10-22
ArXiv (preprint)
arxiv.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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