Scholar
Julie Josse
Google Scholar ID: AlIkUSAAAAAJ
Senior Researcher Inria,
Citations & Impact
Citations
15,772
H-index
38
i10-index
71
Publications
20
Co-authors
10
list available
Publications
Resume (English only)
Academic Achievements
- Awarded the “Young Researcher” Prize by the French Academy of Sciences
- Selected publications include:
- — 'Risk ratio, odds ratio, risk difference… Which causal measure is easier to generalize?' (2023, under revision)
- — 'Causal inference for combining randomized trials & observational data: a review' (Statistical Science, 2020)
- — 'Doubly robust treatment effect estimation with incomplete confounders' (Annals of Applied Statistics, 2020)
- — 'What’s a good imputation to predict with missing values?' (NeurIPS 2021, Spotlight)
- — 'On the consistency of supervised learning with missing values' (Statistical Papers, 2018–2024)
- — 'Bootstrap regularization for low-rank matrix estimation' (JMLR, 2016)
- — 'missMDA: a package to handle missing values' (Journal of Statistical Software, 2015)
- Associate Editor of Foundations and Trends® in Machine Learning; former Associate Editor of Journal of Computational & Graphical Statistics
Research Experience
- Former Professor of Statistics at École Polytechnique, led the Master in Data Science for Business with HEC Business School
- Former visiting researcher at Google Brain
- Over 10 years of collaboration with clinicians to develop machine learning solutions for personalized care
- Involved in Traumatrix: AI-powered decision support tools in ambulances for trauma triage and resource planning
- Led ICUBAM project: app for bed allocation monitoring during the COVID-19 pandemic
- Current industry collaborations: Elixir Health, Theremia, Sanofi, Withings
- Current health collaborations: APHP, CHU Montpellier, Gustave Roussy, Traumabase, etc.
Background
- Senior researcher at Inria (National Institute for Research in Digital Science and Technology)
- Leader of the Inria–Inserm PreMeDiCaL team (Precision Medicine by Data integration and Causal Learning)
- Member of the IdeSP joint research unit with University of Montpellier
- 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
- Also explores multi-modal data analysis, visualization via dimensionality reduction (PCA, correspondence analysis, questionnaire analysis), and low-rank matrix estimation
- Primary applications in biosciences and health
