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
Biomedical natural language processing and information extration
Causal inference

Democratizing machine learning:

Machine learning on dirty data
Missing data in machine learning
Machine-learning model evaluation
Learning on relational databases

Machine learning for mental health, cognition, and brain activity: learning models of brain function and its pathologies from brain imaging.

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 Parietal
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
dirty-cat: Machine learning on dirty categories

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 2021, Clarivate.
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

Editorial duties

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

Conference committees

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
Machine learning for digital humanities at EHESS
Past
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

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.

high-res, more pictures