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.
- 11/20 I have joined DeepMind as a Research Scientist
- 09/20 Our work on estimating optimal transport distances from streams of samples was accepted at NeurIPS 2020.
- 09/20 Our work on estimating mixed Nash equilibria with continuous strategies was accepted at NeurIPS 2020.
- 03/20-06/20 I am working with Inria and AP-HP to help processing data of the Covid-19 pandemic in Paris region.
- 02/20 Our work on accelerating Nash equilibrium finding, with applications to GANs, was accepted at ICML 2020.
- 05/19 Our paper Geometric Losses for Distributional Learning was accepted at ICML 2019 (Paper) (Slides) (Poster)
- 03/19 I am visiting Joan Bruna at NYU CIMS for four months.
- 01/19 Slides for Differentiable Dynamic Programming for Stuctured Prediction and Attention (Paper).
- 11/18 I started working as a post-doctoral researcher with Gabriel Peyré, in ENS, Paris
- 09/18 I defended my PhD thesis (Slides, Thesis) at NeuroSpin. –>
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