About Me

I’m currently a Data Scientist working on computational pathology at Aignostics in Berlin. As such, I mostly develop machine learning models and pipelines to run segmentation and classification on whole slide images depicting human tissue with immune cells and cancer.

Before that I was a PhD student in machine learning at Mines Paris (part of PSL University) and Thales. This research was financed by a Cifre-Défense grant.

Research Interest

My current work applies machine learning to histopathology images.

I’m interested in anomaly detection, also called out-of-distribution detection or one-class classification, and representation learning for signal (Radio, Audio, EEG, ECG) and image processing (satellite, medical).

Publications & abstracts

A novel, scalable deep learning-based approach to automated quality control of multipleximmunofluorescence images

2024 SITC poster (abstract 1283)

One-class classification for low resolution targets discrimination with limited supervision in pulse Doppler radars

Thesis defense 18/01/2023

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

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.

Misc

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.