Better neuroimaging data processing: driven by evidence, open communities, and careful engineering

NeuroHub Seminar Series
Speaker: Gaël Varoquaux
Date: November 19, 2019
Link to recording

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. 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.

Event Details on Ludmer Centre