Research interests

Having a degree in telecommunications and one in space science and engineering, the main emphasis of my work consists in designing image processing techniques to solve various problems in the fields of astrophysics and cosmology. My research mostly focuses on strong gravitational lensing, galaxy clustering and cluster lensing for the astrophysics and cosmology part. To address issues in these areas, my favourite tools are sparse optimisation, source separation and machine learning.

Strong Gravitational Lensing

Strong gravitational lensing s observed when looking at distant galaxies that lay at the behind a massive object, like a galaxy cluster or another galaxy. The massive object will act as a lens by distorting the path of any light-ray approaching it.

More about Strong Gravitational Lensing here.

Galaxy Clusters

Galaxy clusters are the largest known structures in the Universe. They are formed by a large number of galaxies coming in close proximity from one another ending up being gravitationally bound.

More about gravitational lensing and my activity in this area here

Sparse Optimisation

Sparse optimisation, which is one of my favourite tools, consists in decomposing a signal in the smallest possible number of chosen elements in order to make its reconstruction or transmission easier.

More about sparsity here



« Multi-band morpho-Spectral Component Analysis Deblending Tool » is a python package developed to separate objects with different colours in multi-band observations. The code, described in Joseph, Courbin & Starck 2016, assumes that images can be viewed as a linear combination of surface brightnesses emitting with different spectral energy distributions (or colours). Using this principle, we are able to separate overlapping objects with different colours by solving an inverse problem.

This technique was applied to the Hubble Frontier Fields in an unpublish work, of which you may get a glimpse here.


« Sparse Lens Inversion Tool » is a python package that inverses lensed images of galaxies while simultaneously separating the lensed image from the foreground lens light. The algorithm relies on the sparsity of the lens and source galaxies in their respective dictionaries to separate them.

About Me

Originally from a small village in the centre of France, I graduated from Telecom Bretagne (France) with a master’s degree in engineering of telecommunication and from University College London (UK) with a master’s degree in space science and engineering. Despite this very technical education, I enrolled in a PhD program in astrophysics at EPFL (Switzerland) where I applied my skills in image processing and inverse problem solving to the problems met by astrophysicists and cosmologists. In my current assignment in Princeton University, I pursue the development of deblending techniques in view of the processing of the Terra bits of soon to be brought by the LSST telescope.
My personal time goes to the various sports I practice, mostly: Badminton,  Bouldering, Running, Hiking, Skiing and Fencing. I have been practising the latter for more than 20 years now. I have a first level coach degree in fencing which allows me to assist the master (main coach) in his classes and am also involved in the life of the local fencing society as a secretary. I practice mostly sportive fencing, with proficiency in all three weapons (foil, sabre and épée), but I also try to promote and practice artistic, choreographed fencing.


Welcome to my office

Publications & Contributions