About me:   machine learning and brain imaging researcher

Machine learning and brain imaging researcher

Academic research

Machine learning to link cognition with brain activity: I am interested in data mining of functional brain images (fMRI) to learn models of brain function.

  • Encoding and decoding models of cognition
  • Resting-state and functional connectivity
  • Functional parcellations of the brain
  • Spatial penalties for learning and denoising

Democratizing machine learning:

  • Machine learning on dirty data
  • Missing data in machine learning
  • Learning semantic relations

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

Contact

NeuroSpin, CEA Saclay, Bât 145, 91191 Gif-sur-Yvette France
++ 33-1-69-08-79-68


Impact

Awards

  • 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.

Open Hub profile for GaelVaroquaux

Major grants

  • DirtyData (500 k€): machine learning on data without preliminary cleaning.
  • NiConnect (700 k€): statistical tools for clinical research with brain functional connectivity (finished).
  • LearnClues LabCom (300 k€): joint lab with tinyclues, to develop machine learning tools in Python, in big-data and semi-large scale distributed settings (finished).


Professional service

Editorial duties

  • Editor: NeuroImage, Frontiers in brain imaging methods, Frontiers in neuroinformatics
  • Guest editor:  Journal of Computational Science
  • Reviewer: NeuroImage, Human Brain Mapping, Trends in cognitive science, NeuroInformatics, J. Physiology Paris, J. Machine Learning Research, J. Statistical Software, Medical Image Analysis, IEEE Transactions in Medical Imaging, Computing in Science and Engineering, Computer Physics Communications

Conference committees

  • Chair of the steering committee: PRNI (Pattern Recognition in NeuroImaging)
  • General chair: EuroScipy 2010 and 2011 (Paris)
  • Program chair: IEEE PRNI (Pattern Recognition in NeuroImaging) 2013 (Philadelpia), Scipy 2008 and 2009 (Pasadena)
  • Program committee: IPMI 2015, IEEE MICCAI 2013 (Nagoya) and 2014 (Boston), IEEE PRNI 2014 (Tuebingen), Scipy 2013 and 2014 (Austin), ESCO 2010 and 2012 (Plsen), FEMTEC 2011 (South Lake Tahoe), MMBC 2013 (Nagoya), Py4HPC 2012 (Salt Lake city) and 2013 (Denver), MLINI 2014 (Montreal)

Teaching

  • Brain functional connectivity with fMRI at ENSAE (materials)
  • 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
  • Functional brain connectivity ISMRM course 2013 (slides), OHBM course 2011 and 2012 (slides)
  • Machine learning for neuroimaging in Python PRNI course 2012
  • Python for scientific computing EuroScipy 2010, 2011
  • Mathematical optimization EuroScipy 2012
  • scikit-learn: machine learning in Python EuroScipy 2014, EuroPython 2014, Scipy Argentina 2014, Scipy 2013
  • Mayavi: 3d visualization in Python Scipy India 2011, EuroScipy 2010, Scipy 2011

Major keynote talks

  • Budapest BI 2014 Simple big data, in Python slides
  • Scipy Argentina 2014 Thoughts on succeeding in academia despite doing good software slides
  • PyData London 2014 Building a cutting-edge data processing environment on a budget slides - video
  • BrainHack 2013 Open source scientific software, what, why and how - slides
  • Scipy India 2011 Python for brain mining: machine learning as a tool for neuroscience - slides
  • ESCO 2010 Translational computer science?

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: junior specialist 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 tenured computer-science researcher at Inria. His research develops statistical learning tools for scientific inference. He has pioneered the use of machine learning on brain images to map cognition and brain pathologies. More generally, he develops tools to make the use of machine learning easier, with statistical models suited for real-life, uncurated data, and software development for data science. He is project-lead for scikit-learn, one of the reference machine-learning toolboxes, as well as core contributor to joblib, Mayavi, and nilearn. 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.