Selected publications, and their corresponding open source code, from my previous research life when I studied exoplanets and astronomical high-contrast imaging:

Supervised detection of exoplanets in high-contrast imaging sequences

We reformulate the exoplanet detection task (for angular differential imaging sequences) building on well-established machine learning techniques to take high-contrast imaging post-processing from an unsupervised to a supervised learning context. In this new framework, we presented algorithmic solutions using two different discriminative models: SODIRF (random forests) and SODINN (neural networks). The proposed supervised detection framework outperforms state-of-the-art techniques in the task of discriminating planet signal from speckles. For instance, SODINN improves the true positive rate by a factor ranging from ∼2 to ∼10 wrt. low-rank based approaches, working at the same false positive rate. The code has been publicly released and can be found on GitHub.

SODINN framework

VIP: Vortex Image Processing Package for High-contrast Direct Imaging

The Vortex Image Processing (VIP) library is a python package dedicated to astronomical high-contrast imaging. It relies on the extensive python stack of scientific libraries and aims to provide a flexible framework for high-contrast data and image processing.

Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm

Inspired by recent advances in machine learning algorithms such as robust PCA, we proposed the Low-rank plus Sparse plus Gaussian noise (LLSG) decomposition of angular differential imaging sequences.

PhD Thesis:

Advanced data processing for high-contrast imaging-Pushing exoplanet direct detection limits with machine learning, 2017, Université de Liège. Supervisors: Prof. Jean Surdej, Prof. Marc Van Droogenbroeck and Dr. Olivier Absil. ORBi link.