About#
Project lead for Health Data Warehouse projects and Large Language Models in the data team at the Haute Autorité de Santé.
News#
🎉My paper Step-by-step causal analysis of EHRs to ground decision-making has been published in PLOS digital health ! With the other authors, we explore the main sources of variability when running a sound causal inference analysis on Electronic Health records. I also tried to make it a good tutorial for data analysts interested in applying causal inference methods with observational data.
👨🏫 I am giving half of the
Machine Learning for econometricscourse at the ENSAE in 2025. Course material is avaible on github (heavily inspired by the excellent sklearn mooc). I teach about statistical learning, causal inference and panel data.
Past occupations#
2020 - 2024: Project manager, statistician at the Haute Autorité de Santé (HAS) data mission. Referent on health data warehouse projects, methodological referent in statistics. Assists artificial intelligence projects with literature reviews.
2020 - 2023: PhD student in the SODA team at Inria under the supervision of Gaël Varoquaux (Inria) and Dr. Claire Morgand (ARS-IDF). In my research projects, I have applied methods combining causal inference and statistical learning to data from health data warehouses and medico-administrative databases.
📓 Thesis manuscript : Representations and inference from time-varying routine care data
👨🏫 Presentation for the defense : Slides
2020, Covid19, 1st epidemic wave: Ministry of Health crisis center, various data pipelines and strategic dashboards.
2018 - 2020: Machine learning engineer at the statistical department of the Ministry of Health, DREES, working mainly on the National Health Data System, SNDS.
2018 : At AP-HP, I set up the first pseudonymisation system for medical records based on neural networks.
2017 : Master student at MVA, ENS Paris-Saclay in Artificial Intelligence and at ENSAE in statistical learning.
2014-2017 : Ecole Polytechnique, Applied Mathematics