Arthur Mensch

I am a research scientist at DeepMind, that I have joined at the end of 2020. I work in Paris office.

I was a post-doctoral research at École Normale Supérieure, Paris, in Gabriel Peyré’s lab. I hold a Ph.D. in machine learning, prepared in Inria Parietal, from 2015 to 2018.

I am currently interested in optimization and large-scale deep-learning, and continue to have interest for structured prediction, optimal transport and game theory.

My Ph.D. was obtained under the supervision of Gaël Varoquaux, Julien Mairal and Bertrand Thirion. I developed new stochastic algorithms and multi-task models for terabyte sized fMRI dataset analysis.



Oral presentations



I maintain modl, a package that proposes fast algorithms for sparse and dense matrix factorization, with a scikit-learn compatible API.

I maintain cogspaces, a package that allows to do multi-study decoding in functional MRI. This new analysis approach uses brain-mind associations from many fMRI sources to extract functional networks relevant for mental state prediction.

I am a regular contributer to scikit-learn library, a widely used Python library for general machine learning.

I also contribute to nilearn, a python library that leverages scikit-learn to perform analysis in neuro-imagery.



Deep learning (M2 Data Science - Polytechnique / Paris-Saclay)Permalink

Numerical analysis (ENSAE 1A)