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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 1,400 researchers specializing in machine learning and dedicated to scientific excellence and innovation.

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News

23 Jun 2026
Charles Brindamour and Alanna Watt join Mila's Board of Directors
Picture of Charles Brindamour and Alanna Watt
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18 Jun 2026
Speed Science 2026 Recap: Highlights and Winner Announcement
group picture of all participants and jury member of Mila Speed Science contest
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17 Jun 2026
G+D launches AI Hub in Montréal to advance secure AI for critical infrastructure
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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

Empowering 2D neural network for 3D medical image segmentation via neighborhood information fusion
Qiankun Li
Xiaolong Huang
Yani Zhang
Bo Fang
Duo Hong
Junxin Chen
2026-06-30
Pattern Recognition (published)
doi.org
Wagg: Cost-aware Aggregation of Windowing Operators in Stream Processing
Pritish Mishra
Ruoyu Deng
Alexandre da Silva Veith
Oana Balmau
Eyal de Lara
2026-06-19
International Conference on Mobile Systems, Applications and Services (published)
doi.org
The digital heartbeat: a qualitative descriptive study on women's views on preventing cardiovascular disease in primary care
Ilhem Chaima Bousbiat
Samira Abbasgholizadeh Rahimi
Roland Grad
Charo Rodriguez
BACKGROUND: This empirical study aims to explore women's perspectives on cardiovascular disease and the use of digital health interventions … (see more)(DHIs) for their primary prevention and to gather insights on essential features for developing artificial intelligent-enabled technologies. METHODS: Adopting a qualitative descriptive research design, we conducted 15 semi-structured, in-depth interviews via Zoom with women at higher risk for cardiovascular disease. Participants were women over 40 years old, residing in Quebec, with at least one cardiovascular disease risk factor, and proficient in English. Recruitment was from a McGill University-affiliated clinic. An inductive thematic analysis approach was used for data analysis. RESULTS: Five major themes were identified: (i) understanding cardiovascular disease in a variety of ways, (ii) barriers and challenges to preventing cardiovascular disease in women, (iii) women taking charge of their cardiovascular well-being, (iv) mixed perspectives regarding artificial intelligent-enabled technologies for cardiovascular disease prevention such as Xi-Care, and (v) range of suggestions for the format and design of a prospective artificial intelligent-enabled technologies. CONCLUSIONS: Despite the prevalence of cardiovascular disease, there is a significant knowledge gap among women regarding the chronic nature and manifestations of these diseases. Artificial intelligent-enabled technologies like Xi-Care, with the potential for customization and interactive engagement, could enhance the primary prevention of cardiovascular disease in women, providing valuable insights for the subsequent phases of the project leading to Xi-Care's development.
2026-06-18
Family Practice (published)
doi.org
Engineered Nonheme Iron Enzymes Enable Asymmetric Hydrogenation of Alkenes
Yunfei He
Shuang-Yu Dai
Mei‐Yan Xu
Baixu Ma
Jian Tang
Lizhi Tao
Zhen Liu
Developing biocatalytic systems capable of reducing simple alkenes is highly desirable for synthetic chemistry and biosynthesis, yet existin… (see more)g enzymes remain largely restricted to their ability to convert polarized, electron-deficient substrates. Here, we present a nonheme iron metalloenzyme platform that enables hydrogenation of styrenes, conjugated nitriles and amides, and nonconjugated olefins through a putative iron–hydride mechanism. Starting from the Fe(II)/ α -ketoglutarate-dependent dioxygenase GOX, iterative rounds of directed evolution produced an engineered “alkene hydrogenase” (AHase-6) containing 16 mutations and promoting NaBH 4 -driven reduction across diverse C═C bond motifs. Kinetic analysis indicates that this enzymatic hydrogenation process proceeds via formation of an enzyme–substrate ternary complex through a sequential mechanism. Mechanistic studies further reveal that alkene insertion occurs with regioselectivity governed primarily by substrate electronics and sterics. These findings establish nonheme iron enzymes as an unrecognized scaffold for metal–hydride-based hydrogenation and highlight their potential as sustainable, tunable alternatives to traditional catalytic systems.
2026-06-12
Journal of the American Chemical Society (published)
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.

We believe the future will be shaped by Venture Scientists.

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