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Promoting open-source, from inria to :probabl.

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.

People underestimate how impactful Scikit-learn continues to be

Note

François Chollet rightfully said that people often underestimate the impact of scikit-learn. I give here a few illustrations to back his claim.

A few days ago, François Chollet (the creator of Keras, the library that that democratized deep learning) posted:

Tweet from François Chollet: "People underestimate how impactful scikit-learn continues to be"

Indeed, scikit-learn continues to be the most popular machine …

Comité de l’intelligence artificielle: vision et stratégie nationale

English summary

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


J’ai l’honneur d’être nommé au comité de l’intelligence artificielle du gouvernement Français.

La mission qui nous est confiée d’éclairer l’action publique …

2022, a new scientific adventure: machine learning for health and social sciences

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 team has existed for almost a year, and the vision is nice shaping up. Let me share with you illustrations of where we …

My Mayavi story: discovering open source communities

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 contributors and maintainers but have been …

2021 highlight: Decoding brain activity to new cognitive paradigms

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. But to support a broad conclusion, they must be trained on many studies, a difficult problem given …

Hiring an engineer and post-doc to simplify data science on dirty data

Note

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

It is well known that data cleaning and preparation are a heavy burden to the data scientist.

Dirty data research

In the dirty data project, we have been conducting machine-learning research …

Hiring someone to develop scikit-learn community and industry partners

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. Departing from our usual focus on excellence in algorithms, statistics, or code, we want to add to the team someone with some technical understanding, but an …

2020: my scientific year in review

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. My highlights this year revolve around basic and applied data-science for health.

Highlights …

Technical discussions are hard; a few tips

Note

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

A large software project is above all a social exercise in which technical experts try to reach good decisions together, for instance on github pull requests. But communication is difficult, in …