scikit-learn operations at Inria foundation
Machine learning to link cognition with brain activity: I am interested in data mining of functional brain images (fMRI) to learn models of brain function.
Democratizing machine learning:
Core contributor to scientific computing in Python:
World-wide recognized contributor and project-manager for open-source scientific software.
Gaël Varoquaux is a tenured computer-science researcher at Inria. His research develops statistical learning tools for scientific inference. He has pioneered the use of machine learning on brain images to map cognition and brain pathologies. More generally, he develops tools to make the use of machine learning easier, with statistical models suited for real-life, uncurated data, and software development for data science. He is project-lead for scikit-learn, one of the reference machine-learning toolboxes, as well as core contributor to joblib, Mayavi, and nilearn. 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.
Gaël Varoquaux is an Inria faculty researcher working on data science and brain imaging. He has a joint position at Inria (French Computer Science National research) and in the Neurospin brain research institute. His research focuses on using data and machine learning for scientific inference, applying it to brain-imaging data to understand cognition, as well as developing tools that make it easier for non-specialists to use machine learning. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.
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