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
1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau,
Masters in Quantum Physics
2004: Ecole Normal Supérieure
Ecole Normal Supérieure
2001-2004: undergraduate studies
Gaël Varoquaux is a tenured research director at Inria. His research
focuses on statistical-learning tools for data science and scientific
inference. Since 2008, he has been exploring data-intensive approaches to
understand brain function and mental health. 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.
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.