Democratizing machine learning:
Machine learning for mental health, cognition, and brain activity: learning models of brain function and its pathologies from brain imaging.
Core contributor to scientific computing in Python:
I get a lot of email, and fail to answer all of it. My apologies.
Seeking advice: Do not write to me to ask me about some software, even if I am the maintainer. Write to the relevant mailing list or open a ticket. If you ask me advice on data processing, please be aware that I might not reply, or I might post my reply on a public mailing list or social network, in an attempt to have more people benefit from it.
Job seeking: If you are looking for an internship, write me a concise email, telling me why you are interested in working with me, and with a CV (even if it is only for a preliminary enquiry). Also, check our group's job offer page. I am more likely to reply to people from under-represented groups, because diversity is important to me.
Invitations: I enjoy giving talks and am flattered by invitations. But I am trying to fly no more than once a year, to reduce my carbon footprint. I more likely to give remote talks, or consider traveling if not too far by train from Paris.
World-wide recognized contributor and project-manager for open-source scientific software.
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.
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.