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Inspiring the development of artificial intelligence for the benefit of all

Located in the heart of Quebec’s AI ecosystem, Mila is a community of more than 1,400 researchers specializing in machine learning and dedicated to scientific excellence and innovation.

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Prospective Students

Virtual Information Sessions

Connect with a Mila academic advisor and current student-researchers to learn more about Mila's community and how to join us on August 19, 31 and September 11, 2026.

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Startups

Mila Ventures Launchpad

This program supports AI startups at any time of the year. Benefit from cutting-edge resources and tailored support to accelerate your technology's development.

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Learning

AI Policy Compass

Offered by Mila and the Public Policy Forum, this program is designed to equip policy and decision makers with the tools to navigate the opportunities and risks of AI. The next cohort will be held in French on September 1-2, 2026, at Mila.

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News

03 Aug 2026

Machine Learning for Real-World Change: Celebrating AI4Good Lab’s 10th Cohort

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28 Jul 2026

Summer School in Responsible AI and Human Rights: A Fourth Edition That Shines Internationally

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23 Jul 2026

Using AI to Modernize Property Inspection

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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

1D Pre‐Acquisition Navigator Correcting Respiratory‐Induced Field Fluctuations in Multi‐Echo Gradient‐Echo Imaging of the Thoracic Spinal Cord

Alicia E. Cronin

Alexandre D’Astous

Nathan Williams

Antoine Guénette

Aimee Salakhov

Seth Stubblefield

Colin D. Mcknight

Lipika Narisetti

Subramaniam Sriram

Seth A. Smith

Ryan K. Robison

Guillaume Gilbert

Julien Cohen‐Adad

Kristin P. O’Grady

PURPOSE: In the spinal cord (SC), multi-echo gradient echo (ME-GRE) increases gray (GM) and white matter (WM) contrast and improves sensitiv… (see more)ity to lesions in people with multiple sclerosis (pwMS). However, SC ME-GRE is susceptible to breathing-induced field fluctuations, causing ghosting artifacts and signal loss. Recent work introduced a 1D phase navigator following the last echo to measure field variations; however, susceptibility to phase wrapping increases at longer echo times. We propose a 1D phase navigator preceding the first echo, reducing phase accumulation and eliminating the need for respiratory monitoring. METHODS: ME-GRE data covering the lower (T9-T12 vertebrae) and upper (T4-T8 vertebrae) thoracic SC were acquired in 20 healthy volunteers and 3 pwMS at 3T. Standard and navigator-corrected images were acquired in the same acquisition. To evaluate image quality, WM and GM signal-to-noise ratio (SNR), WM/GM contrast-to-noise ratio (CNR), and background ghosting signals were measured and compared between the two reconstructions. Both were blindly assessed for artifacts, structural delineation, and diagnostic confidence in pwMS. RESULTS: Navigator correction significantly increased GM and WM SNR and CNR, reduced posterior ghosting across both thoracic regions, and significantly reduced artifacts while increasing structural delineation. Preliminary evaluation in three pwMS showed consistent improvements in artifact mitigation, structural delineation, and lesion conspicuity with navigator correction, providing proof-of-concept for potential clinical application. CONCLUSION: A 1D navigator prior to the first echo reduces ghosting and improves thoracic SC image quality without respiratory monitoring. This approach could improve the diagnostic value and enhance the reliability of thoracic SC ME-GRE.

2026-07-23

Magnetic Resonance in Medicine (published)

doi.org

Controllable and Content-Based Recommendations

Fırat Öncel

Jihoon Jeong

Emiliano Penaloza

Mirco Ravanelli

Laurent Charlin

Cem Subakan

Traditional recommendation systems rely on latent (dense) representations, making them difficult to interpret and control. We propose the Co… (see more)ntrollable and Content-Based Recommendations (CCBR) framework, which builds its recommendations from textual user profile representations. CCBR plugs into collaborative filtering models and introduces controllability via text bottlenecks. We show that CCBR enables text-based and multimodal interventions, allowing users to steer the model towards the directions they prefer. Different from existing controllable recommendation systems, CCBR infers the text summaries directly from item contents (images, audio or video). Across image-, audio-, and video-based datasets, we demonstrate that the proposed framework obtains competitive model performance with standard (latent-representation) models while providing controllable model summaries via text. The model also outperforms TEARS, a recent baseline for controllable recommendation systems. Through systematic interventions, we demonstrate the efficacy of the user steering mechanism.

2026-07-22

arXiv (preprint)

doi.org

arxiv.org

Cortical microstructural integrity predicts an exploitation bias in older adulthood

Patrick Hewan

Alfie Wearn

Jeremy Hogeveen

Kayla Williams

R Nathan Spreng

Gary R. Turner

Sylvia Villeneuve

Judes Poirier

John C S Breitner

Sylvain Baillet

Andrée-Ann Baril

Bellec Pierre

Véronique Bohbot

Danilo Bzdok

Mallar Chakravarty

D Louis Collins

Mahsa Dadar

Simon Ducharme

Alan Evans

Claudine Gauthier … (see 80 more)

Maiya R Geddes

Rick Hoge

Yasser Ituria‐Medina

Gerhard Multhaup

Lisa-Marie Münter

Natasha Rajah

Pedro Rosa-Neto

Taylor Schmitz

Soucy Jp

Nathan Spreng

Christine Tardif

Etienne Vachon-Presseau

Mohammadali Javanray

Meishan Ai

Philippe Amouyel

Nicholas Ashton

Gabriel Aumont‐Rodrigue

Julie Bailly

Guilia Baracchini

Kaj Blennow

Christian Bocti

Lianne Boisvert

Sophie Boutin

Ann Brinkmalm Westman

A P Dagher

Xing Dai

Samir Das

Marina Dauar‐Tedeschi

Louis De Beaumont

Christine Déry

Maxime Descoteaux

Elena Drobotea

M Elie

Alfonso Fajardo Valdez

Vladimir Fonov

David Morgan

Jonathan Gallago

Greco Cr

Louise Hudon

Gabriel Jean

Anne Labonté

Robert Laforce

Marc Lalancette

Jean-Charles Lambert

Jeannie‐Marie Leoutsakos

Danaé Lussier Dumouchel

B Misic

Béry Mohammediyan

Holly NewboldFox

Eugenia Nita Capota

Alix Noly‐Gandon

Adrian Eduardo Noriega de la Colina

Pierre Orban

Valentin Ourry

Cynthia Picard

Alexa Pichet Binette

A. L. Poirier

Nathalie Prenevost

Ting Qiu

Marc James Quesnel

Charles Ramassamy

Jean‐Michel Raoult

Jordana Remz

Safa Sanami

Frederic St‐Onge

Cherie Strikwerda‐Brown

Elisabeth Sylvain

Andràs Tikàsz

Christina Tremblay

Stefanie Tremblay

Jennifer Tremblay‐Mercier

Stéphanie Tullo

Irem Ulku

Paolo Vitali

Yara Yakoub

Robert Zatorre

Henrik Zetterberg

Pierre Bellec

Jean-Paul Soucy

Claudia Greco

OBJECTIVES: Prefrontal regions are implicated in explore-exploit decision-making during foraging. Older adults often show an exploitation bi… (see more)as, and this age period is also marked by deteriorating prefrontal myelination. To investigate whether these phenomena are linked, we examined whether lower magnetization transfer saturation (MTsat), a myelin-sensitive quantitative MRI (qMRI) measure, in these regions predicts greater exploitation bias during foraging, and whether cortical microstructure is a better predictor of bias than macrostructure (i.e., cortical thickness). METHODS: Cognitively healthy older adults with familial risk of Alzheimer's disease (AD) (N=118, 60-88 years) completed a foraging task indexing explore-exploit decision-making. qMRI was used to derive MTsat values for the frontopolar cortex (FPC), medial orbitofrontal cortex (OFC), rostral middle frontal gyrus (rMFG), dorsal anterior cingulate cortex (dACC), as well as the locus coeruleus (LC), a core subcortical region strongly implicated in explore-exploit decision-making. Secondary analyses examined associations between available AD risk markers and foraging. RESULTS: Lower MTsat in the FPC, OFC, rMFG, and LC was associated with an exploitation bias, with LC and FPC emerging as the strongest predictors. No relationship was observed for the dACC. MTsat remained a significant predictor of foraging after controlling for cortical thickness. Observed associations were largely unrelated to AD risk markers. DISCUSSION: Individual differences in cortical microstructural integrity within a well-defined explore-exploit circuit are associated with an exploitative decision-making bias in older adults. These findings highlight the value of qMRI microstructural integrity markers, beyond standard macrostructural assays, in characterizing the neural correlates of exploitation biases in later life.

2026-07-22

Journals of Gerontology Series B: Psychological Sciences and Social Sciences (published)

doi.org

High-resolution dissection of concept acquisition in different families of protein language models

Shawn Whitfield

Tom Marty

Robert M. Vernon

Christopher J. Langmead

Dhanya Sridhar

Quentin Fournier

Protein language models have been increasingly successful on tasks ranging from fitness prediction to functional design, yet what biological… (see more) knowledge they acquire and where it is encoded within their internal representations remain underexplored. Through a high-resolution layer-by-layer interpretability analysis of 8 models from the ESM2 and AMPLIFY families on 22 concepts from human proteome annotations, we found that these models encode concepts of increasing levels of complexity along their depth: basic physicochemical properties and linear motifs are best captured by early-layer embeddings, secondary structure from subsequent layers, and domain-level semantics from middle layers. Principal component projections of these embeddings showed that they separate biologically meaningful protein groupings, and molecular-biology-inspired interventions demonstrated that pLM embeddings can discriminate phosphomimic-active from inactive mutants. Perhaps surprisingly, we observed that pretraining data and compute had a greater impact on the linear emergence of biological concepts than scaling up parameters. By revealing where biological knowledge is captured in pLMs and which choices shape its emergence, our work offers insights to develop more robust, biologically grounded protein language models.

2026-07-22

bioRxiv (accepted)

doi.org

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Mila Ventures

Mila Ventures

Our venture arm cultivates the next generation of companies backed by Mila's world-class AI research ecosystem. We invest in visionary founders building at the frontier of deep tech, AI, STEM, and beyond.

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