I am supported by DIM Math Innov PhD Fellowship.
I am working on statistical learning methods in application. In particular, I am currently developing Deep Learning algorithms to perform risk screening over large databases, applied to SNIIRAM, a large observational database containing electronic health records in France.
ZiMM: a deep learning model for long term and blurry relapses with non-clinical claims data, published Journal of Biomedical Informatics, 2020. arXiv:1911.05346, Joint work with A.Kabeshova, B.Lukacs, E.Bacry, S.Gaïffas.
- Short version presented at Machine Learning for Health Workshop at Neural Information Processing Systems (NeurIPS 2019 - ML4H)
2019 – 2020
- Probabilités discrètes, L3, Université de Paris (18h)
- Statistiques et simulation, L3, Université de Paris (42h)