About me:   machine learning researcher

Machine learning researcher

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 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

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


Inria Saclay, 1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau, France



  • 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


Open source

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

Open Hub profile for GaelVaroquaux

Professional service

Editorial duties

Conference committees

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


  • Machine learning with scikit-learn MOOC Materials
  • Machine learning for digital humanities at EHESS
  • Past
  • Machine learning with scikit-learn at ENSAE (materials)

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


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.

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