I am a CNRS Associate Researcher at the Astrophysics Research Center of Lyon (CRAL, Observatory of Lyon). I am developing algorithms to detect, characterize and reconstruct objects from faint signals embedded in multi-variate data. The main application of my current research work is direct imaging at high contrast and at high angular resolution for exoplanet detection and for the reconstruction of the circumstellar environment.
Previously (2021-09/2022), I was a postdoc in a pluridisciplinary project with LESIA (Observatory of Paris) and INRIA (Willow team). Before that, I was a postdoc at CRAL (2020). Between 2016-2019, I was a PhD student at the Hubert Curien Laboratory. I developed a set of methods to detect, characterize and reconstruct faint signals in image series. There are based on a common methodological guideline including: statistics-based modeling of the nuisance component, physics-based modeling of the instrumental effects, robust strategies to deal with the presence of outliers, and dedicated processings of temporo-spectral information redundancy. I applied this methodology both in astrophysics and in holographic microscopy.
PhD in Image & Vision, 2019
University of Lyon, University of Saint-Etienne
Engineer degree in Signal, Image, Communication, Multimedia, 2015
Phelma, Grenoble INP
Workshops, Seminars, etc.
Detecting and characterizing objects in images in the low signal-to-noise ratio regime is a critical issue in many areas such as astronomy or microscopy. In astronomy, the detection of exoplanets and their characterization by direct imaging from the Earth is a hot topic. A target star and its close environment (hosting potential exoplanets) are observed on short exposures. In microscopy, in-line holography is a cost-effective method for characterizing microscopic objects. Based on the recording of a hologram, it allows a digital focusing in any plane of the imaged 3-D volume. In these two fields, the object detection problem is made difficult by the low contrast between the objects and the nonstationary background of the recorded images.
In this thesis, we propose an unsupervised exoplanet detection and characterization algorithm based on the statistical modeling of background fluctuations. The method, based on a modeling of the statistical distribution of patches, captures their spatial covariances. It reaches a performance superior to state-of-the-art techniques on several datasets of the European high-contrast imager SPHERE operating at the Very Large Telescope. It produces statistically grounded and spatially-stationary detection maps in which detections can be performed at a constant probability of false alarm. It also produces photometrically unbiased spectral energy distributions of the detected sources. The use of a statistical model of the data leads to reliable photometric and astrometric accuracies. This methodological framework can be adapted to the detection of spatially-extended patterns in strong structured background, such as the diffraction patterns in holographic microscopy. We also propose robust approaches based on weighting strategies to reduce the influence of the numerous outliers present in real data. We show on holographic videos that the proposed weighting approach achieves a bias/variance tradeoff. In astronomy, the robustness improves the performance of our detection method in particular at close separations where the stellar residuals dominate. Our algorithms are adapted to benefit from the possible spectral diversity of the data, which improves the detection and characterization performance. All the algorithms developed are unsupervised: weighting and/or regularization parameters are estimated in a data-driven fashion. Beyond the applications in astronomy and microscopy, the signal processing methodologies introduced are general and could be applied to other detection and estimation problems.
The European Very Large Telescope
The Unit Telescopes
The VLT/SPHERE instrument