We use cookies to analyze the browsing and usage of our website and to personalize your experience. You can disable these technologies at any time, but this may limit certain functionalities of the site. Read our Privacy Policy for more information.

Setting cookies You can enable and disable the types of cookies you wish to accept. However certain choices you make could affect the services offered on our sites (e.g. suggestions, personalised ads, etc.). Essential cookies These cookies are necessary for the operation of the site and cannot be deactivated. (Still active) Toggle Analytics cookies Do you accept the use of cookies to measure the audience of our sites?

Toggle Multimedia Player

Do you accept the use of cookies to display and allow you to watch the video content hosted by our partners (YouTube, etc.)?

Toggle Home

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.

About Featured Featured Featured Featured Featured

Featured Featured 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.

Register 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. Apply now 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. Register now News 03 Aug 2026 Machine Learning for Real-World Change: Celebrating AI4Good Lab’s 10th Cohort Read the story 28 Jul 2026 Summer School in Responsible AI and Human Rights: A Fourth Edition That Shines Internationally Read the story 23 Jul 2026 Using AI to Modernize Property Inspection Read the story See more news 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.

Browse the online directory 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 cooperative 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 See more publications 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.

We believe the future will be shaped by Venture Scientists. Learn more

📌 Lead AI Applications Developer / Senior AI Developer, Safety (Montreal)
🏢 Effective Altruism Global
📍 Montreal