NeuroSpin, CEA Saclay, Bât 145, 91191 Gif-sur-Yvette 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: NeuroImage, Human Brain Mapping, Trends in cognitive science, NeuroInformatics, J. Physiology Paris, J. Machine Learning Research, J. Statistical Software, Medical Image Analysis, IEEE Transactions in Medical Imaging, Computing in Science and Engineering, Computer Physics Communications
Chair of the steering committee: PRNI (Pattern Recognition in NeuroImaging)
General chair: EuroScipy 2010 and 2011 (Paris)
Program chair: IEEE PRNI (Pattern Recognition in NeuroImaging) 2013 (Philadelpia), Scipy 2008 and 2009 (Pasadena)
Program committee: IPMI 2015, IEEE MICCAI 2013 (Nagoya) and 2014 (Boston), IEEE PRNI 2014 (Tuebingen), Scipy 2013 and 2014 (Austin), ESCO 2010 and 2012 (Plsen), FEMTEC 2011 (South Lake Tahoe), MMBC 2013 (Nagoya), Py4HPC 2012 (Salt Lake city) and 2013 (Denver), MLINI 2014 (Montreal)
Brain functional connectivity with fMRI at ENSAE (materials)
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 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.
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.