Bellec Pierre
Contact information
Biography
Pierre Bellec is a Full Professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal and a regular researcher at the Centre de recherche de l’Institut universitaire de gériatrie de Montréal (CRIUGM). He is also an associate member of Mila – the Quebec Artificial Intelligence Institute – where he contributes to the interface between neuroscience and machine learning.
Pierre Bellec holds a Ph.D. in medical imaging from Université Paris XI and pursued his postdoctoral training at McGill University (Montreal Neurological Institute). He is also an agrégé in Mathematics, certified by the French Ministry of National Education and Research (2000, France).
He is an Associate Professor in the Department of Psychology at Université de Montréal and an associate member of Mila – the Quebec Artificial Intelligence Institute – where he contributes to the interface between neuroscience and machine learning.
At CRIUGM, he directs the SIMEXP lab (Simulation, Exploration and Modeling of the Brain), which develops computational tools to analyze large-scale neuroimaging data. He plays an active role in several initiatives, including the Courtois NeuroMod project, where he serves as principal investigator.
Research interests
His research interests lie at the intersection of computational neuroscience, functional neuroimaging, and artificial intelligence. He is particularly focused on:
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the functional organization of the human brain using functional magnetic resonance imaging (fMRI);
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the development of algorithms and statistical methods for analyzing large neuroimaging datasets;
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machine learning applied to neuroscience, especially for predicting cognitive or clinical traits from brain connectivity;
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open science, through the sharing of tools, data, and reproducible pipelines (e.g., through the NeuroLibre project);
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neurodegenerative diseases such as Alzheimer’s disease, where his methods are used to identify potential functional biomarkers.
Keywords: artificial intelligence, machine learning, brain imaging, neurodegeneration biomarkers, neural modeling, brain data analysis.
