About me:   machine learning researcher

Machine learning researcher

Research Interests

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

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

Democratizing machine learning:

Machine learning and public health:

  • Analytics on public-health databases for personalized medicine and treatment development

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


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



  • Prix de l'Académie des Sciences du transfer
    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, ICML, ICLR, AISTATS, AAAI, Senior Program Committee IJCAI.
  • Past
    • 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 for digital humanities at EHESS
  • Machine learning with scikit-learn at ENSAE (materials)
  • Past

Major keynote talks

See my slideshare page

Education and previous positions

  • 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 tenured research director at Inria. His research focuses on statistical-learning tools for data science and scientific inference. Since 2008, he has been exploring data-intensive approaches to understand brain function and mental health. More generally, he develops tools to make machine learning easier, with statistical models suited for real-life, uncurated data, and software for data science. 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 contributed key methods for learning on spatial data, matrix factorizations, and modeling covariance matrices. He has a PhD in quantum physics and is a graduate from Ecole Normale Superieure, Paris.

Hacker version
Gaël Varoquaux is an Inria faculty researcher working on data science and brain imaging. He has a joint position at Inria (French Computer Science National research) and in the Neurospin brain research institute. His research focuses on using data and machine learning for scientific inference, applying it to brain-imaging data to understand cognition, as well as developing tools that make it easier for non-specialists to use machine learning. 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.