science posts

Science must drive the narratives that shape society

I would like to take a brief moment to reflect on what drives me as an academic.

Academia’s root are in creating knowledge and sharing it. We, academics, have a role to play in shaping society. In computer science, we sometimes focus on the creation of technology. Here, creation …

AI super-intelligent to play Go, and math?

Since 2017, an AI has been defeating the best Go experts, despite the game being particularly challenging. Such “super intelligence” is rare, but it could also emerge in fundamental mathematics.

Note

This post was originally published in French as part of my scientific chronicle in Les Echos.

Imitation is not …

AI for health: the impossible necessity of unbiased data

Is unbiased data important to build health AI? Yes!

Can there be unbiased data? No!

Building health on biased data discriminates

The notion of bias depends on the intended use.


In medicine, we have seen the importance of tuning devices and decisions for the target population. The problem is not …

2024 highlights: of computer science and society

Note

For me, 2024 was full of back and forth between research, software, and connecting these to society. Here, I lay out some highlights on AI and society, as well as research and software, around tabular AI and language models.

As 2025 starts, I’m looking back on 2024. It …

When AIs must overcome the data

Improving conversational artificial intelligences or simpler prediction engines involves overcoming biases, that is, going beyond the limits of data. But the notion of bias is subtle, as it depends on the goals.

Image generated with "ChatGPT", with the prompt "Please generate an image of a robot arm wrestling a figure made of numbers. This figure does not look like a robot, but more like a human, however it is made of numbers."

Note

This post was originally published in French as part of my scientific chronicle in Les Echos.

In …

Do AIs reason or recite?

Despite their apparent intelligence, conversational artificial intelligences often lack logic. The debate rages on: do they reason or do they recite snatches of text memorized on the Internet?

Image generated with "ChatGPT", with the prompt "Please generate an image of a robot with a stream of numbers coming out of his mouth. The robot is on the left, facing right, and the numbers flow, as if they were sound."

Note

This post was originally published in French as part of my scientific chronicle in Les Echos. I updated it with new …

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 …

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 …

Survey of machine-learning experimental methods at NeurIPS2019 and ICLR2020

Note

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

How do machine-learning researchers run their empirical validation? In the context of a push for improved reproducibility and benchmarking, this question is important to develop new tools for model comparison. We …

2019: my scientific year in review

My current research spans wide: from brain sciences to core data science. My overall interest is to build methodology drawing insights from data for questions that have often been addressed qualitatively. If I can highlight a few publications from 2019 [1], the common thread would be computational statistics, from dirty …

Comparing distributions: Kernels estimate good representations, l1 distances give good tests

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Given two set of observations, are they drawn from the same distribution? Our paper Comparing distributions: l1 geometry improves kernel two-sample testing at the NeurIPS 2019 conference revisits this classic statistical problem known as “two-sample testing”.

This post explains the context and the paper with a bit of hand …

Getting a big scientific prize for open-source software

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

A few days ago, Loïc Estève, Alexandre Gramfort, Olivier Grisel, Bertrand Thirion, and myself received the “Académie des Sciences Inria prize for transfer”, for our contributions to the scikit-learn project …

2018: my scientific year in review

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). Rather than going over everything that we did in 2018, I would like to give a few highlights: We published major work using machine learning to …

Our research in 2017: personal scientific highlights

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 subjects, decoding based on the raw time-series of brain activity, and a personnal …

Beyond computational reproducibility, let us aim for reusability

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Scientific progress calls for reproducing results. Due to limited resources, this is difficult even in computational sciences. Yet, reproducibility is only a means to an end. It is not enough by itself to enable new scientific results. Rather, new discoveries must build on reuse and modification of the state …

Our research in 2016: personal scientific highlights

Year 2016 has been productive for science in my team. Here are some personal highlights: bridging artificial intelligence tools to human cognition, markers of neuropsychiatric conditions from brain activity at rest, algorithmic speedups for matrix factorization on huge datasets…


Artificial-intelligence convolutional networks map well the human visual system

Eickenberg et …

Of software and Science. Reproducible science: what, why, and how

At MLOSS 15 we brainstormed on reproducible science, discussing why we care about software in computer science. Here is a summary blending notes from the discussions with my opinion.

“Without engineering, science is not more than philosophy”     —     the community

How do we enable better Science? Why do we do software …