I am a senior researcher at Inria (National research center for digital science) and the leader of the Inria – Inserm (National research center for health) PreMeDiCaL (Precision Medicine by Data integration and Causal Learning) team and member of Idesp joint with University of Montpellier. Previously, I was a Professor of Statistics at Ecole Polytechnique, responsible of a Master in Data Science for Business with HEC Business school and a visiting researcher at Google Brain. I have been working for over 10 years with clinicians to personalize care through the development of machine learning solution.
News: see also PreMeDICaL’s news
– I have received a prize « Young Researcher » by the French Academy of Science! More info + video of the coupole and the republican guard!
– My PhD student Margaux Zaffran has won the L’Oreal Unesco Young Talent Prize —my second student to receive this honor.
Project highlights:
Traumatrix: AI-powered decision support tools embedded in ambulances to optimize patient triage, care, and resource planning for trauma cases. See Video, slides, statitical challenges, team retreat, more info. Consortium Capgemini Invent, Traumabase, EHESS, CNRS, Ecole Polytechnique, Inria.
ICUBAM Development of an app for Bed Allocation Monitoring during COVID-19 Fork. slides.
Research talks:
Causal inferences: Transporting treatment effects from clinical trials to broader populations, NIH Biostat. National Cancer Institute (slides)
Video at Online Causal Inference Seminar: « Leveraging incomplete RCT and observational data »
Missing values: EPFL 2023, AutoML2022 ICML slides, video. A missing values tour: 2022 slides les diableret PhD school, 2019 useR slides, 2019 video (start at 30′)
Researchers/Interns/Phd/Postdoc/Engineers positions. Contact me julie.josse[at]inria.fr
My research focuses on missing data methods (EM algorithms, imputation, supervised learning), causal inference (treatment effect estimation, integrating RCTs with observational data, survival analysis, policy learning), and uncertainty quantification. I also explore multi-modal data analysis, visualization through dimensionality reduction techniques (PCA, correspondence analysis, questionnaire analysis), and low-rank matrix estimation. My primary applications lie in the bio-sciences and health domains. Short CV. Detailed CV