Jeroen received his PhD in Artificial Intelligence for Astronomy & Astrophysics from KU Leuven (Belgium) in 2023 and spent part of his PhD at MIT. His research primarily focuses on the development of machine learning methods for the classification of astronomical time series from space missions. In particular he works on classifying the light curves that are being delivered by the NASA TESS and Kepler missions according to their stellar variability types. In addition, he works on developing novel supervised and unsupervised machine learning methods (e.g., by leveraging techniques from the biomedical domain) to better understand pulsating stars. The latter type of stars are the focus of the field of asteroseismology and are of prime importance for astrophysics as they can be used to improve stellar structure and evolution models.  

Selected publications:

• Audenaert & Tkachenko, 2022, Multiscale entropy analysis of astronomical time series. Discovering subclusters of hybrid pulsators, Astronomy & Astrophysics, Volume 666, id.A76, 14 pp.

• Audenaert et al., 2021, TESS Data for Asteroseismology (T’DA) Stellar Variability Classification Pipeline: Setup and Application to the Kepler Q9 Data, The Astronomical Journal, Volume 162, Issue 5, id.209, 25 pp.

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