I’m currently a PhD student in machine learning at Mines Paris (part of PSL University) and Thales.
I’m financed by a Cifre-Défense grant, and my PhD is expected to end in Dec. 2022.
My current research applies machine learning to radar data.
I’m especially interested in anomaly detection, also called out-of-distribution detection or one-class classification, and machine learning for signal and image processing.
Near OOD detection for low-resolution radar micro-Doppler signatures
2022 ECML PKDD (previous URL leads to the expanded version on arXiv)
Paper experiments code
Dataset generation code
Deep random projection outlyingness for unsupervised anomaly detection
2021 ICML UDL Workshop (under review for publication)
From unsupervised to semi-supervised anomaly detection methods for HRRP targets
2020 IEEE Radar Conference (RadarConf20)
Teaching assistant for the data science course of Mines Paris engineering program - 2nd semester of 2021-2022.
Teaching assistant for the deep learning for image analysis course of Mines Paris engineering program and IASD master program - fall 2021 and winter 2021-2022.
Teaching assistant for the probability course of Mines Paris engineering program - 1st semester of 2020-2021.
I talked about anomaly detection at a Franco-German University workshop on mathematical image processing in May 2022 in Kaiserslautern.
I contributed to the review of a paper for the ECML PKDD 2022 conference.
I co-supervised a master degree intern working on multivariate time series discrimination at Thales during the spring and summer of 2021.
I participated in the ICLR Computational Geometry & Topology Challenge 2021, which resulted in a white paper.
I talked about anomaly detection to IASD master program students as part of the deep learning for image analysis course during the winter 2020 edition.
I contributed to the review of a paper for the RADAR 2019 conference.