varoquaux@normalesupWelcome, the navigation bar on the right should help you find what you are looking for.
Personal information
I am a computational scientist working in brain imaging.
Contact details
Phone: ++ 33-1-69-08-79-68
Parietal team, INRIA
Laboratoire de Neuro-Imagerie Assistée par Ordinateur
NeuroSpin
CEA Saclay , Bât 145, 91191 Gif-sur-Yvette France
- My CV (updated August 2010)
- Personal pages: some accounts of travels around the world
- My blog: scientific computing with Python, as well as personal and professional updates.
Previous research: Physics
I hold a Ph.D. in Physics. Until 2008, I was doing research on developing quantum-based atom-optic metrology, in particular, using quantum gases for atom-interferometric inertial sensing.
More on the physics page...
Current research: Brain imaging and modeling
My current research is on building quantitative statistical and descriptive models of brain activity through functional imaging.
I develop algorithms and models for extracting salient and reproducible features from functional MRI images without using a paradigm, such as in resting-state studies. I focus on group models opening the door to between-subject comparisons.
In addition, I am working on using graphical models to do inter-subject or inter-group comparison of connectivity structures.
More on the neuroscience page...
Scientific computing activities
Software I contribute to
I am a core contributor in a number of scientific computing libraries in Python:
- Mayavi: 3D plotting and scientific visualization in Python.
- joblib: meta-programming for lightweight pipelining of scientific jobs in Python.
- scikit-learn: Machine learning in Python.
- nipy: NeuroImaging data analysis tools in Python.
- pyreport: literate programming for scientific code in Python.
Personal views on scientific computing
My contributions to the scientific computing software ecosystem are motivated by my vision on computational science.
Scientific research relies more and more on computing. However, most of the researchers are not software engineers, and as computing is becoming ubiquitous, the limiting factor becomes more and more the human factor [G. Wilson, 2006] [P. Norvig, 2009].
Note
To address the needs of computing accross scientific fields, I believe that we need a general-purpose, high-level, interactive, and highly-readable language and set of tools for scientific computing.
- C does not answer my needs: does a molecular biologist know about pointers? Should she?
- Matlab does not answer my needs either: scientific work with computers is not only about numerical computation. Have you tried writing an experiment-control software with Matlab? How about file management? Inserting the algorithms in a web server.
We need better teaching material, that sit at interfaces between software engineer, and general science. Most top notch tools and libraries are full of domain-specific jargon and conventions.
For reproducible science, we need the code to be readable and to reflect the corresponding scientific operation. We need it to be unit-tested to ensure its correctness.
Note
We need to start considering scientific libraries as end-result of our research with the same importance than articles [J. Buckheit and D. Donoho. 1995]. They need to convey a scientific message, to be understandable and refutable. New results should be reproducible via published code [CISE Jan. 2009]. As for established algorithms, scientific libraries with their documentation and examples should be the textbooks of tomorrow.
Further reading:
- Open source Machine Learning software [S. Sonnenburg et al. 2007]
- Open source mathematical sofware [D. Joyner and W. Stein 2007]
- Licensing, intellectual property in scientific work [A. Gonzalez 2006]
- Scientific software development best practices [S. Baxter et al. 2006]
Publications
Latest publications: ( Link to complete publication list )
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