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
  • Causal inference

Machine learning research:

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

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


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



  • 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


Open source

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

Open Hub profile for GaelVaroquaux

Professional service

Editorial duties

  • Editor: ElifePreviously: 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
    • 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
    • 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


    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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