About me: machine learning researcher

Machine learning researcher & enterpreneur

Research director (DR, HDR), soda, INRIA
Chief Science Officier of :Probabl. (a scikit-learn enterprise)

Bio

Gaël Varoquaux is a research director working on data science at Inria (French computer science national research) where he leads the Soda team. He is also co-founder and chief science officer of Probabl.
Varoquaux's research covers fundamentals of artificial intelligence, statistical learning, natural language processing, causal inference, as well as applications to health, with a current focus on public health and epidemiology. He also creates technology: he co-founded scikit-learn, one of the reference machine-learning toolboxes, and helped build various central tools for data analysis in Python.
Varoquaux has worked at UC Berkeley, McGill, and university of Florence. He did a PhD in quantum physics supervised by Alain Aspect and is a graduate from Ecole Normale Superieure, Paris.

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Research Interests

Machine learning and AI research:

Table Foundation models, Tabular relational learning, Tabular AI
Evaluation of AI, including in uncertain settings
Machine learning on dirty data
Missing data in machine learning

Health AI

Analytics on health databases for public health and medicine
Causal inference and survival analysis methods
Machine learning and neuroscience: mental health, cognition, and brain activity

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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

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Created and was editor of the scipy lecture notes until 2022.
Old notes on my view on scientific computing.

Contact

email

firstname.lastname@inria.fr

I get a lot of email, and really struggle to answer. My apologies. In addition, I am on part-time leave (as :Probabl.'s CSO), which means I have reduced bandwidth.

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 will likely 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 job in research, be advice that I am recieve a lot of request. Check our group's job offer page, and if one is open with my name, write an email to all the person listed, with a CV and motivation letter. If you are looking for an industry job, check our positions and apply there. If you send me an email that is not related to an open position, I might not reply.

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 & memberships

Member of the Academia Europaea
Doctorats honoris causa, UC Louvain 2025.
Highly-cited researcher, Clarivate, since 2021, 2022, 2023, 2024.
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

Program committee: NeurIPS (area chair), ICML (meta reviewer), ICLR (area chair), AISTATS, AAAI, Senior Program Committee IJCAI.
Editor: Elife (2018- 2021) — Editor NeuroImage (2014-2017); Editor Frontiers in NeuroInformatics (2013-2016); Guest editor: Machine learning journal, Journal of Computational Science
Workshops

Table Representation Learning, NeurIPS 2023 workshop
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