I graduated from the french engineering school CentraleSupélec with a major background in applied mathematics and algorithmic. After a year of working as a research engineer in Thales, I decided to begin a Ph.D. program in Université Paris Saclay, at the Institut d'Astrophysique Spatiale, under the supervision of Nabila Aghanim (IAS) and Aurélien Decelle (UCM, LRI).
I am currently a postdoctoral researcher at the École Normale Supérieure (ENS) in Paris working on the interfaces between statistical physics and machine learning algorithms in Giulio Biroli's team at the center for data sciences.
Ph.D. in Astrophysics & Cosmology, 2021
Engineering school, 2017
AI for physics and physics for AI: development of AI-based tools for astrophysics and cosmology and exploration of the links between theoretical physics and AI for a better understanding of AI systems. Teaching duty under the data science program of PSL University.
Applying statistical physics models for the understanding of machine learning algorithms.
Cosmic web environments: identification, characterisation and quantification of cosmological information.
Conception and development of unsupervised algorithms to deinterleave radar pulses collected by satellites.
This paper exposes the reachable accuracies of the redshift-space constraints provided by the several environments of the cosmic web with respect to the matter monopole and quadrupole statistics. By splitting the density field into its cosmic web components, one can tighten down the constraints by factors up to 5.5 for the summed neutrino mass.
We use a new approach based on self-supervised deep learning networks originally applied to transparency separation in order to …
This work aims at extracting the cosmological information content of the several cosmic web environments. While we know that the matter power spectrum is not containing all the information about hte underlying cosmological model, we can wonder wether the environments are enclosing different types of information that one can use to break some of the degeneracies among parameters of the model. In particular, we show that a simple two-point correlator becomes sensitive to higher-order features when we have a look at the environments instead of the full matter distribution.
The extraction of patterns from spatially structured point-cloud datasets is ubiquitous in many fields of science. In this work, we address the case of extracting one-dimensional structure from such data by formulating the problems in terms of a regularised version of a mixture model.
The late-time matter distribution depicts a complex pattern commonly called the cosmic web. In this picture, the spatial arrangement of …
The way clusters are embedded in the cosmic web is linked with their intrinsic structure and dynamics. In this study, we carry an analysis of the links between the connectivity, the number of filaments a cluster is connected to, and their shape and dynamics (ellipticity, accretion rate etc.). In particular, we report a correlation between the connectivity and the assembly history of clusters with young and perturbed clusters being more connected than the old and relaxed ones.
The task of clustering point-cloud data is nowadays believed to be either easy to carry or uninformative because the lack of knowledge (number of clusters, sizes, etc.) on the underlying pattern. This work proposes to use a statistical physics formulation of the clustering performed by means of a Gaussian Mixture Model to alleviate some of the drawbacks of the clustering task. In particular, it shows that we can explore the dataset to obtain several key information on the number of clusters, their size and how they are embedded in space, even in high dimensions.
How to extract filaments based on a sparse and discrete spatial distribution of matter tracers? This paper proposes an algorithm for doing so by relying on a regularised graph to obtain a smooth one-dimensional structure representing the filamentary pattern of the cosmic web.