Inria Saclay Île-De-France, 1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau, France
I get a lot of email. Do not write to me to ask me about some software, even if I am the maintainer. Write to the relevent mailing list or open a ticket. If you are looking for an internship, write me a concise email, telling me why you are interested in working with me, and with a CV (even if it is only for a preliminary enquiry). Also, check our group's job offer page.
Reviewer: PNAS, Annals of Applied Statistics, Nature Methods, NeuroImage, NeuroInformatics, J. Physiology Paris, J. Machine Learning Research, J. Statistical Software, Medical Image Analysis, IEEE Transactions in Medical Imaging, Human Brain Mapping, Trends in cognitive science, Computing in Science and Engineering, Computer Physics Communications
Program committee: NeuroIPS, ICML, Senior Program Committee IJCAI. — Formely: ICASSP, IPMI, IEEE MICCAI, IEEE PRNI, Scipy, ESCO, FEMTEC, Py4HPC
Chair of the steering committee: PRNI (Pattern Recognition in NeuroImaging), 2014
General chair: EuroScipy 2010 and 2011 (Paris)
Program chair: IEEE PRNI (Pattern Recognition in NeuroImaging) 2013 (Philadelpia), Scipy 2008 and 2009 (Pasadena)
Machine learning with scikit-learn at ENSAE (materials)
Statistics with Python at the CogMaster masters in cognitive science, ENS Paris (materials)
Masters in Quantum Physics 2004: Ecole Normal Supérieure
Ecole Normal Supérieure 2001-2004: undergraduate studies
IEEE style 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.
Hacker version 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.