Lune Bellec
Full Professor In Psychology Professeure Titulaire En Psychologie at Université de Montréal
Montreal, Quebec, Canada
Summary
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Senior🎓
Top SchoolLune Bellec is a Full Professor in Psychology at Université de Montréal with 18 years of experience developing machine learning tools to model brain structure and function from MRI and to train artificial neural networks that mimic individual human brain activity and behavior. She leads large-scale neuroimaging data collection and the international CNeuroMod collaborative effort, including an innovative video-game-based cognitive task blending Super Mario–style design with reinforcement learning. Her work spans computational modelling, fMRI methodology and open-source contributions—she has improved visualization and usability in notable neuroimaging tooling like nilearn. Funded by major Canadian and international agencies and with a background bridging physics, computer vision and mathematics, she combines deep methodological rigor with creative experimental paradigms to study brain reorganization across the lifespan.
18 years of coding experience
19 years of employment as a software developer
Bachelor Mathematics, Bachelor Mathematics at Université de Rennes IDoctorat Physics speciality in medical imaging, Doctorat Physics speciality in medical imaging at Paris-Sud University (Paris XI)Master Computer vision Mathematics Learning, Master Computer vision Mathematics Learning at Ecole normale supérieure de Cachan
English, French
Github Skills (8)
data-visualizations10
machine-learning10
data-visualization10
data-visualisation10
python10
neuroimaging10
brain-imaging9
matplotlib9
Programming languages (13)
Github contributions (5)
nilearn/nilearn
Jun 2018 - Feb 2019
Machine learning for NeuroImaging in Python
Role in this project:
Data Scientist
Data ScientistContributions:6 reviews, 117 commits, 9 PRs in 8 months
Contributions summary:Lune's contributions primarily focused on improving the visualization capabilities within the nilearn library. Their work included fixing a typo in the warning message, enhancing the surface plotting documentation, and switching plotting tools from papaya to brainsprite, indicating a focus on improving user experience. They also made improvements to code to adhere to PEP8 standards and modularization efforts.
neuroimagingpythondecodingbrain-connectivitymachine-learning
SIMEXP/nilearn
Sep 2018 - Sep 2019
Machine learning for NeuroImaging in Python
Contributions:14 PRs, 52 pushes in 11 months
neural-networkneuroimagingpythonmachine-learning
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