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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

02 Sep 2025
Hugo Larochelle becomes the new Scientific Director of Mila
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20 Aug 2025
Mila Welcomes Inaugural AI Policy Fellowship Cohort
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28 Jul 2025
Mila at ACL 2025
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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

Intersecting perspectives: A participatory street review framework for urban inclusivity
Rashid A. Mushkani
Shin (Alexandre) Koseki
2025-10-01
Habitat International (published)
doi.org
Intersecting perspectives: A participatory street review framework for urban inclusivity
Rashid A. Mushkani
Shin (Alexandre) Koseki
2025-10-01
Habitat International (published)
doi.org
Intersecting perspectives: A participatory street review framework for urban inclusivity
Rashid A. Mushkani
Shin (Alexandre) Koseki
2025-10-01
Habitat International (published)
doi.org
Rootlets-based registration to the PAM50 spinal cord template
Sandrine Bédard
Jan Valosek
Valeria Oliva
Kenneth A. Weber
Julien Cohen-Adad
Abstract Spinal cord functional MRI studies require precise localization of spinal levels for reliable voxel-wise group analyses. Traditiona… (see more)l template-based registration of the spinal cord uses intervertebral discs for alignment. However, substantial anatomical variability across individuals exists between vertebral and spinal levels. This study proposes a novel registration approach that leverages spinal nerve rootlets to improve alignment accuracy and reproducibility across individuals. We developed a registration method leveraging dorsal cervical rootlets segmentation and aligning them non-linearly with the PAM50 spinal cord template. Validation was performed on a multi-subject, multi-site dataset (n = 267, 44 sites) and a multi-subject dataset with various neck positions (n = 10, 3 sessions). We further validated the method on task-based functional MRI (n = 23) to compare group-level activation maps using rootlet-based registration to traditional disc-based methods. Rootlet-based registration showed superior alignment across individuals compared with the traditional disc-based method on n = 226 individuals, and on n = 176 individuals for morphological analyses. Notably, rootlet positions were more stable across neck positions. Group-level analysis of task-based functional MRI using rootlet-based registration increased Z scores and activation cluster size compared with disc-based registration (number of active voxels from 3292 to 7978). Rootlet-based registration enhances both inter- and intra-subject anatomical alignment and yields better spatial normalization for group-level fMRI analyses. Our findings highlight the potential of rootlet-based registration to improve the precision and reliability of spinal cord neuroimaging group analysis.
2025-08-26
Imaging Neuroscience (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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