I get a lot of email, and fail to answer all of it. My
apologies.
Seeking advice: Do not write to me to ask me about some
software, even if I
am the maintainer. Write to the relevant mailing list or
open a ticket. If you ask me advice on data
processing,
please be aware that I might not reply, or I might post
my reply on a public
mailing list or social network, in an attempt to have
more people benefit from it.
Job seeking: 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. I am more likely to reply to people from
under-represented groups, because diversity is important
to me.
Invitations: I enjoy giving talks and
am flattered by invitations. But I am trying to fly no
more than once a year, to reduce my carbon
footprint. I more likely to give remote talks,
or consider traveling if not too far by train from Paris.
Inria Saclay,
1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau,
France
Masters in Quantum Physics
2004: Ecole Normal Supérieure
Ecole Normal Supérieure
2001-2004: undergraduate studies
Bio
IEEE style
Gaël Varoquaux is a research director working on data science at Inria
(French Computer Science National research) where he leads the Soda team on computational and
statistical methods to understand health and society with data. Varoquaux
is an expert in machine learning, with an eye on applications in health
and social science. He develops tools to make machine learning easier,
suited for real-life, messy data. He
co-funded scikit-learn, one of the reference machine-learning toolboxes,
and helped build various central tools for data analysis in Python. He
currently develops data-intensive approaches for epidemiology and public
health, and worked for 10 years on machine learning for brain function
and mental health. Varoquaux has a PhD in quantum physics supervised by
Alain Aspect and
is a graduate from Ecole Normale Superieure, Paris.
Hacker version
Gaël Varoquaux is a research director working on data science and health
at Inria (French Computer Science National research). His research
focuses on using data and machine learning for scientific inference, with
applications to health and social science, as well as developing tools
that make it easier for non-specialists to use machine learning. He has
long applied it to brain-imaging data to understand cognition. 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.