About me

I am an observational cosmologist using gravitational lensing to answer fundamental questions about the Universe. I am currently a postdoctoral fellow of the Swiss National Science Foundation (SNSF) at Stanford University. Previously, I received my Ph.D. from the École Polytechnique Fédérale de Lausanne (EPFL). My research interest mainly focuses on cosmology with strong lensing and image processing. I am also interested in studying the physics of accretion disks with gravitational microlensing.

Besides research, I love spending my weekends exploring the mountains of the French Jura, or more recently, the Californian Sierra Nevada, for climbing, hiking, or skiing.


Research interests

Time-delay Cosmography & the Hubble Constant

As part of the TDCOSMO collaboration, I am studying strongly lensed quasars to measure the Hubble constant, i.e. the expansion rate of the Universe. We are doing this by measuring the time delays between the multiple images of the quasar to obtain precise estimates of the cosmological parameters.

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Black holes and accretion disks

My research involves using microlensing to study the structure of accretion disks around supermassive black holes. Microlensing occurs when a star passes in front of one of the multiple images of a lensed quasar. By analyzing this signal, we obtain valuable insights into the accretion disk's characteristics.

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Galaxies and Quasars co-evolution

Lensing is a very precise tool to measure the mass of galaxies. I am using this phenomenon to study the relation between quasars and their host galaxies in the distant Universe.

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Image processing and deconvolution

Atmospheric turbulence blurs the images we are taking from ground-based telescopes. I am interested in developing algorithms to correct for this effect and obtain sharper images.

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PyCS3 is python package for measuring time delays between light curves. It includes a flexible way of modelling microlensing variations, which are often observed in strongly lensed quasars light curves.

PyCS3 repository


STARRED is an image deconvolution python package. It allows us to obtain sharper images and precise point-source photometry.

STARRED repository