Gaël Varoquaux

Note Probabl’s get together, in falls 2025 I’m thrilled to announce that I’m stepping up as Probabl’s CSO (Chief Science Officer) to supercharge scikit-learn and its ecosystem, pursuing my dreams of tools that help go from data to impact.

At Maïc’s 100th birthday, I asked her “you lived 100 years, what was the most important change for you?”.

I have recently been awarded France’s national order of merit, for my career, in science, in open source, and around AI.

Note TabICL is a state-of-the-art tabular learner [Qu et al 2025].

Note For me, 2024 was full of back and forth between research, software, and connecting these to society.

Note Foundation models, pretrained and readily usable for many downstream tasks, have changed the way we process text, images, and sound.

We just released skrub 0.2.0.

Note Open-source efforts around scikit-learn at Inria are spinning off to a new enterprise, Probabl, in charge of sustainable development of a data-science commons.

Note François Chollet rightfully said that people often underestimate the impact of scikit-learn.

English summary I have been appointed to the government-level panel of experts on AI, to set the national vision and strategy in France.

A retrospective on last year (2022): I embarked on a new scientific adventure, assembling a team focused on developing machine learning for health and social science.

The Mayavi Python software, and my personal history: A thread on Python and scipy ecosystems, building open source codebase, and meeting really cool and friendly people I am writing today as a goodbye to the project: I used to be one of the core contribut…

Broad decoding models that can specialize to discriminate closely-related mental process with limited data TL;DR Decoding models can help isolating which mental processes are implied by the activation of given brain structures.

Note Join us to work on reinventing data-science practices and tools to produce robust analysis with less data curation.

Note With the growth of scikit-learn and the wider PyData ecosystem, we want to recruit in the Inria scikit-learn team for a new role.

The year 2020 has undoubtedly been interesting: the covid19 pandemic stroke while I was on a work sabbatical in Montréal, at the MNI and the MILA, and it pushed further my interest in machine learning for health-care.

Note This post discuss the difficulties of communicating while developing open-source projects and tries to gives some simple advice.

Jean Dechoux was born between the first and the second world wars, in a small French town, close to Germany.

Note A simple survey asking authors of two leading machine-learning conferences a few quantitative questions on their experimental procedures.

My current research spans wide: from brain sciences to core data science.

Note Given two set of observations, are they drawn from the same distribution?

Note An important acknowledgement for a different view of doing science: open, collaborative, and more than a proof of concept.

From a scientific perspective, 2018 [1] was once again extremely exciting thank to awesome collaborators (at Inria, with DirtyData, and our local scikit-learn team).

We have just announced that a foundation will be supporting scikit-learn at Inria [1]: scikit-learn.fondation-inria.fr Growth and sustainability This is an exciting turn for us, because it enables us to receive private funding.

Two weeks ago, we held a scikit-learn sprint in Austin and Paris.

In my opinion the scientific highlights of 2017 for my team were on multivariate predictive analysis for brain imaging: a brain decoder more efficient and faster than alternatives, improvement clinical predictions by predicting jointly multiple traits of…

Note Scientific progress calls for reproducing results.

Two week ago, we held in Paris a large international sprint on scikit-learn.

Year 2016 has been productive for science in my team.

To my friends developing data science for the social media, marketing, and advertising industries, It is time to accept that we have our share of responsibility in the outcome of the US elections and the vote on Brexit.

I have built static binaries of Unison 2.48 for ARM Run on my NAS, the arm architecture is necessary to synchronize with the recent Ubuntu. unison-2.48.4-armel.zip Warning I will not support these binaries I will not answer any questions or request on the…

Problem setting: persistence for big data Joblib is a powerful Python package for management of computation: parallel computing, caching, and primitives for out-of-core computing.

At MLOSS 15 we brainstormed on reproducible science, discussing why we care about software in computer science.

Nilearn’s goals Make advanced machine learning techniques easy for neuroimaging research.

My research group is looking to fill a post-doc position on learning biomarkers from functional connectivity.

Note The 2015 edition of the machine learning open source software (MLOSS) workshop was full of very mature discussions that I strive to report here.

A couple of weeks ago, we had in Paris the second international nilearn sprint, dedicated to making machine learning in neuroimaging easier and more powerful.

Note tl;dr: Reproducibilty is a noble cause and scientific software a promising vessel.

Note This year again we will have an exciting workshop on the leading-edge machine-learning open-source software.

We, Parietal team at INRIA, are recruiting software developers to work on open source machine learning and neuroimaging software in Python.

EuroScipy 2015, the annual conference on Python in science will take place in Cambridge, UK on 26-30 August 2015.

PRNI (Pattern Recognition for NeuroImaging) is an IEEE conference about applying pattern recognition and machine learning to brain imaging.

I am moving my website to a new design, relying on Pelican and more modern CSS.

Here are some references on software development techniques and patterns to help write better code.

Work with us to leverage leading-edge machine learning for neuroimaging At Parietal, my research team, we work on improving the way brain images are analyzed, for medical diagnostic purposes, or to understand the brain better.

A week ago, the 2014 edition of the scikit-learn sprint was held in Paris.

We have just released the 0.15 version of scikit-learn.

I’d like to welcome the four students that were accepted for the GSoC this year: Issam: Extending Neural networks Hamzeh: Sparse Support for Ensemble Methods Manoj: Making Linear models faster Maheshakya: Locality Sensitive Hashing Welcome to all of you.

Work with us on putting machine learning in the hand of cognitive scientists Parietal is a research team that creates advanced data analysis to mine functional brain images and solve medical and cognitive science problems.

Christophe Pradal, Hans Peter Langtangen, and myself recently edited a version of the Journal of Computational Science on scientific software, in particular those written in Python.