About me: machine learning researcher

Machine learning researcher

Research director (DR, HDR), soda, INRIA
Director of the scikit-learn operations at Inria foundation

Research Interests

Machine learning and public health:

Analytics on health databases for personalized medicine and treatment development
Causal inference

Machine learning research:

Machine learning on dirty data
Missing data in machine learning
Machine-learning model evaluation
Tabular relational learning and NLP

Machine learning for mental health, cognition, and brain activity:

Biomarkers of mental traits and disorders
Resting-state and functional connectivity
Encoding and decoding models of cognition

More...

My publications page and my Google scholar page.
Research at Soda
My —infrequently updated— CV

Open-source software

Core contributor to scientific computing in Python:

scikit-learn: Machine learning in Python
joblib: lightweight pipelining of scientific code
Mayavi: 3D plotting and scientific visualization
nilearn: Machine learning for NeuroImaging
skrub: Prepping tables for machine learning

More...

I am editor of the scipy lecture notes.
See my view on scientific computing.

Contact

email

firstname.lastname@inria.fr

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

Impact

Awards

Highly-cited researcher, Clarivate, 2021, 2022.
Prix de l'Académie des Sciences du transfer
2019, The French National Academie of Science.
François Erbsmann Prize Honnorable mention, 2013
The most prestigious award in medical imaging.
Nominated member of the Python Software Foundation
FOSS India award 2008 shared with Prabhu Ramachandran, for Mayavi

Bibliometry

Open source

World-wide recognized contributor and project-manager for open-source scientific software.

Professional service

Expert at the National committee on AI
Expert at the Global Partnership on AI (OECD), 2020-2021

Editorial duties

Editor: Elife —Previously: Editor NeuroImage (2014-2017); Editor Frontiers in NeuroInformatics (2013-2016); Guest editor: Machine learning journal, Journal of Computational Science

Program committee: NeurIPS (area chair), ICML, ICLR, AISTATS, AAAI, Senior Program Committee IJCAI.
Workshops

Automating Data Science in the face of messy data, ECML 2021 workshop
The art of learning with missing values, ICML 2020 workshop

Past(selected list)

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)

Teaching

Machine learning with scikit-learn MOOC Materials
Past

Machine learning for digital humanities at EHESS
Machine learning with scikit-learn at ENSAE (materials)
Statistics with Python at the CogMaster masters in cognitive science, ENS Paris (materials)
Functional brain connectivity at the Bio-Medical Engineering master, Telecom ParisTech

Major keynote talks

See my speakerdeck

Older talks on slideshare

Education and previous positions

McGill MNI 2019-2020: Visiting professor
Mila Mila 2019-2020: Visiting professor
INSERM unicog 2010-2011: Post-doc on pronostics with resting-state fMRI
INRIA parietal 2008-2010: Post-doc on resting-state fMRI methods
UC Berkeley 2008: software engineer on nipy
Consultant in scientific computing 2008: Enthought
Marie Curie European Fellow 2007-2008, with Massimo Inguscio at LENS, Florence
PhD in Quantum Physics 2005-2007: Université Paris-Sud Orsay, supervision Alain Aspect, topic: Atomic sources for long-time-of-flight interferometric inertial sensors
Masters in Quantum Physics 2004: Ecole Normal Supérieure
Ecole Normal Supérieure 2001-2004: undergraduate studies

Bio

Gaël Varoquaux is a research director working on data science at Inria (French Computer Science National research) where he leads the Soda team. Varoquaux's research covers on the one hand fundamentals of artificial intelligence, statistical learning, natural language processing, causal inference, as well as application to health, with a current focus on public health and epidemiology. He also creates technology: 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 a PhD in quantum physics supervised by Alain Aspect and is a graduate from Ecole Normale Superieure, Paris.

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