The Killam Seminar Series presents: TBA
The Killam Seminar Series presents: TBA
Tue, Feb 21, 4:00 PM
Montreal Neurological Institute – Hospital • Montréal, QC
Free
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NeuroHub Seminar Gael Varoquaux
Better neuroimaging data processing: driven by evidence, open communities, and careful engineering
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de Grandpré Communications Centre, Montreal Neurological institute and Hospital 3801 University Street Montréal, QC H3A 2B4 Canada
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About this event
NeuroHub Seminar Series
Better neuroimaging data processing: driven by evidence, open communities, and careful engineering
Speaker: Dr Gael Varoquaux, Research Director - INRIA, France; Visiting Scholar at the MNI and MILA
Tuesday, Nov. 19, 2019 @ 1pm
Location: de Grandpré Communications Centre, the Montreal Neurological institute and Hospital (The Neuro), 3801 University Street, Montreal, QC, H3A 2B4, Canada
Free event: Open to anyone interested in the subject matter. Aimed at faculty, staff and students from research institutes across greater Montreal area.
Abstract: Data processing is a significant part of a neuroimaging study. The choice of corresponding methods and tools is crucial. I will give an opinionated view how on a path to building better data processing for neuroimaging. I will take examples on endeavors that I contributed to: defining standards for functional-connectivity analysis, the nilearn neuroimaging tool, http://neuroquery.org, the scikit-learn machine-learning toolbox -an industry standard with a billion regular users. I will cover not only the technical process -statistics, signal processing, software engineering- but also the epistemology of methods development. Methods govern our results, they are more than a technical detail.
Bio: Gaël Varoquaux is a tenured computer-science researcher at Inria (L'Institut national de recherche en informatique et en automatique) . His research focuses on statistical learning tools for data science and scientific inference. He has pioneered the use of machine learning on brain images to map cognition and pathologies. More generally, he develops tools to make machine learning easier, with statistical models suited for real-life, uncurated data, and software for data science. He co-funded scikit-learn, one of the reference machine-learning toolboxes, and helped build various central tools for data analysis in Python. Varoquaux has contributed key methods for learning on spatial data, matrix factorizations, and modeling covariance matrices. He has a PhD in quantum physics and is a graduate from Ecole Normale Superieure, Paris.
More information @ http://ludmercentre.ca/events
Contact: Tel: 514 398-3956; Email: jean-baptiste.poline@mcgill.ca
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