I am a Senior Researcher at Inria (National research center in digital science) and the head of the Inria-Inserm Idesp (Inserm – Um)  (National research center in health) team PreMeDICaL (precision medicine by data integration and causal learning) and responsible of the Causal and Missing Data group at Inria.
News: see also PreMeDICaL’s news.
– My PhD student Margaux Zaffran received the l’Oreal Unesco Young talent prize!! This is my 2nd PhD student receiving this prize!
Team retreat: before launching the clinical trial to assess the decision support tool Traumatrix, we tested the models a last time with clinicians
– Talk on causal measures and transportability, NIH Biostatistics National Cancer Institute (slides)
Video at Online Causal Inference  Seminar on 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.

Project TrauMatrix decision tool for intensive Care. Consortium Capgemini Invent, Traumabase, EHESS, CNRS, Ecole Polytechnique, Inria
– ICUBAM Development of an app for Bed Allocation Monitoring  during COVID-19 Fork. slides.
–  R-mis-static website [paper], with missing values ressources (lecture, workflows, tutorials, etc.), Contribute!
Causal Inference Taskview to organize R packages, Contribute!
Rforwards dedicated to widen the participation of the minorities in communities.

julie.josse[at]inria.fr   – Office, 231,  INRIA Montpellier.

My main research fields are: missing values (EM algorithms, imputation, supervised learning), causal inference (treatment effect estimation, combining RCT and observational data; survival analysis, policy learning),  visualization with dimensionality reduction (PCA, correspondence analysis, questionnaire analyses, multi­-blocks data), low rank matrix estimation. Main application in  bio-sciences and health.  Detailed CV

Research

Selected publications:

  • Colnet, B.,Varoquaux, G., J.J. &  Scornet, E.. Risk ratio, odds ratio, risk difference… Which causal measure is easier to generalize? In revision. 2023.
  • Colnet, B. et al. Causal inference methods for combining randomized trials and observational studies: a review. Statistical Science. 2020. 
  • Mayer, I., J.J., Wager, S., Sverdr, E., Traumabase.  Doubly robust treatment effect estimation with incomplete confounders. Annals Of Applied Statistics. 2020.
  • Le Morvan, J.J, E. Scornet & . What’s a good imputation to predict with missing values? Neurips 2021. (Spotlight).
  • J.J., et al. On the consistency of supervised learning with missing values. Statistical paper. 2018-2024.
  • J.J & Wager, S. Bootstrap-Based Regularization for Low-Rank Matrix Estimation. Journal of Machine Learning research. 2016.
  • J.J & Husson, F. missMDA a package to handle missing values (imputation with PCA, PCA with missing values). Journal of Statistical Software. 2015

Current researchers collaborators: Judith AbecassisGosia Bogdan, Claire Boyer,  Antoine ChambazYanniv Romano, Erwan Scornet, Bertrand ThirionShu Yang,
Current collaboration with companies EDF, Elixir Health, Quinten Health, Sanofi,  etc.
Current collaboration in health:   APHP, CHU Nancy/Montpellier, Gustave Roussy, Traumabase, etc.
Associate Editor: Foundations and Trends® in Machine Learning. Past:  Journal of Computational & Graphical Statistics.  Journal of Statistical Software. (7 years). AC for Neurips, ICLR.

– A summary of my research contributions up to 2023  can be found here.
– An overview of my research up to 2016 can be found in my Habilitation.  (slides)

Publications:
YearAuthors Title link
2023Zhao, P., Gatulle, N, James, A., Josse, J. & Chambaz, A.
2023Bénard, C., Naf, J. & Josse, J.MMD-based Variable Importance for Distributional Random Forest
pdf
2023 Sussman, H., Chambaz, A. & Josse, J. Adaptive Conformal, an R package for adaptive conformal inferencepdf
2023Zhao, P., Chambaz, A., Josse, J., Yang, S. Positivity-free Policy Learning with Observational Datapdf
2023Bénard, C, Josse, J.Variable importance for causal forests: breaking down
the heterogeneity of treatment effects
pdf
2023Colnet, B, Josse, J., Varoquaux, G., Scornet, E. Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?
Submitted.
pdf
2023Zaffran, Josse, J. M., Dieuleveut A., Romano, Y. Conformal prediction with missing values.
ICML2023.
pdf
poster
2023Zhao, P., Josse, J. & Yang, S. (2023). Efficient and robust transfer learning of optimal individualized
treatment regimes with right-censored survival data.
Submitted.
pdf
2022Colnet, B, Josse, J., Varoquaux, G., Scornet, E. Reweighting the RCT for generalization: finite sample analysis and variable selection.
Submitted.
pdf
2022Blet et al.Association between in-ICU red blood cells transfusion and one-year mortality in ICU survivors.
Critical Care.
pdf
2022Colnet, B, Josse, J., Varoquaux, G., Scornet, E. Generalizing a causal effect: sensitivity analysis and missing covariates.
Journal of Causal Inference.
pdf
slides
2022Gauss et al. Is Early Norepinephrine Associated with 24-hour Mortality of Blunt Trauma Patients in Haemorrhagic Shock? An International Cohort Study.
Jama Network.
pdf
2022Garaix et al.Decision-making tools for healthcare structures in times of pandemic.
Anaesthesia Critical Care & Pain Medicine.
pdf
2022Zaffran et al. Adaptive conformal prediction for time series.
ICML2022.
pdf
slides
video
2022Perez-Lebel et al. Benchmarking missing-values approaches for predictive models on health databases.
GigaScience.
pdf
2021Le Morvan, J. Josse, E. Scornet. & G. VaroquauxWhat’s a good imputation to predict with missing
values?
Neurips 2021. (Spotlight).
pdf
video
slides
2021Sportisse, A. et al. Model-based Clustering with Missing Not At Random Data.
Submitted.
pdf
2021Mayer, I., Josse, J & TraumbaseTransporting treatment effects with incomplete attributes.
Biometrical Journal
pdf
2020-2023Colnet, B et al.Causal inference methods for combining randomized trials and observational studies: a review.
Statistical Science.
pdf
2020Le Morvan, J. Josse, M., Moreaux, T, E. Scornet. & G. VaroquauxNeumiss networks: differential programming for supervised learning with missing values. Neurips2020. (Oral) pdf
slides
video
slides
video
2020Sbidian et al. Hydroxychloroquine with or without azithromycin and in-hospital mortality or discharge in patients hospitalized for COVID-19 infection: a cohort study of 4,642 in-patients in France.
Preprint.
pdf
2020Consortium ICUBAMICU Bed Availability Monitoring and analysis in the Grand Est région of France during the COVID-19 epidemic.
Statistiques et Société.
pdf
slides
2020A. Sportisse, C. Boyer,
and Josse, J.
Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data.
Neurips2020.
pdf
slides
video
code
2020A. Sportisse, C. Boyer, A. Dieuleveut, J. Josse.Debiasing Stochastic Gradient Descent to handle missing values. Neurips2020. pdf
slides
2020J.D. Moyer et al. Trauma reloaded: Trauma registry in the era of data science. Anaesthesia Critical Care & Pain Medicine. pdf
2020Muzellec, B., Josse, J. Boyer, C. & Cuturi, M.
Missing Data Imputation using Optimal Transport.
ICML2020.
pdf
slides
videos
code
2019Josse, J., Mayer, I, & Vert, J.P.MissDeepCausal: causal inference from incomplete data using deep latent variable models.
Preprint.
pdf
2020Le Morvan, M., N. Prost, J. Josse, E. Scornet. & G. VaroquauxLinear predictor on linearly-generated data with missing values: non consistency and solutions.
AISTAT2020.
pdf
slides
2020Descloux, P. , Boyer, C. Josse, J. Sportisse, A. Sardy, S. Robust Lasso-Zero for sparse corruption and model selection with missing covariates.
Scandinavian Journal of Statistics.
pdf
2022Mayer, I, Sportisse, A., Josse, J., Vialaneix, N., Tierney, N. R-miss-tastic: a unified platform for missing values methods and workflows. R journal.
pdf
2019-20Mayer, I, Josse, J., Wager, S., Sverdr, E., Moyer, J.D. and Gauss, T. Doubly robust treatment effect estimation with incomplete confounders.
Annals Of Applied Statistics.
pdf
code
slides
videos
2019-21M. Bogdan, W. Jiang, J. Josse, B. Miasojedow and V. Rockova.Adaptive Bayesian SLOPE – High dimensional Model Selection with Missing Values.
Journal of Computational and Graphical Statistics.
pdf
slides
2019Josse, J., Prost, N., Scornet, E. & Varoquaux, G. On the consistency of supervised learning with missing values.
Preprint.
pdf
slidescode
slides
2019G. Robin, O. Klopp, J. Josse, E. Moulines, and R. Tibshirani Main effects and interactions in mixed and incomplete data frames.
Journal of the American Statistical Association.
pdf
Package
2019Hamada, S et al.Effect of Fibrinogen administration on early mortality in traumatic haemorrhagic shock: a propensity score analysis.
Journal of Trauma.
2019Sportisse, A., Boyer, C. and Josse, J.Low-rank estimation with missing non at random data.
Statistics and Computing.
pdf
code
2018Josse, J., Husson, F. Robin, G. and Balasubramanian. N.Imputation of mixed data with multilevel SVD.
Journal of Computational and Graphical Statistics.
pdf
slides
2018Robin, G, Sardy, S., Moulines, E. and Josse, J. Low-rank model with covariates for count data
with missing values.
Journal of Multivariate Analysis.
pdf
Package
code

2018Jiang, W., Lavielle, M. Josse, J. and T. Gauss.Logistic Regression with Missing Covariates -- Parameter Estimation, Model Selection and Prediction within a Joint-Modeling Framework.
CSDA.
pdf
slides
Package, code
2018 G. Robin, Hoi To Wai, J. Josse, O. Klopp and E. MoulinesLow-rank interactions and sparse additive effects model for large data frames.
NeurIPS 2018.
2018Josse, J. and Reiter, J.Introduction to the Special Section on Missing Data.
Statistical Sciences.
pdf
2018Seijo-Pardo, B., Alonso-Betanzos, A., P. Bennett, K. Bol\'on-Canedo, Josse, J., Saeed, M., Guyon, I. Feature selection in the presence of missing data.
Neurocomputing, ESANN.
2017-2018Mozharovskyi, P., Husson, F. and Josse, J.Nonparametric imputation by data depth.
Journal of the American Statistical Association.
pdf
slides
code
2017Holmes, S and Josse, J.50 years of data-sciences, discussion.
Journal of Computational and Graphical Statistics.
pdf
2017Bollmann, S., Cook, Di. Dumas, J., Fox, J., Josse, J., Keyes, O. Strobl, C., Turner, H. and Debelak, R.A First Survey on the Diversity of the R Community.
R journal.
pdf
slides
2017G. Celeux, J. Jewson, J. Josse, J.M. Marin and C. P. Robert.Some discussions on the Read Paper "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig.

pdf
2017Foulley, JL, Celeux, G and Josse, J.Empirical Bayes approaches to PageRank type
algorithms for rating scientific journals.
Technical report.
pdf
slides
2016Sobczyk, P, Bogdan, M. and Josse, J.PCA using penalized semi-integrated likelihood.
Journal of Computational and Graphical Statistics.
pdf
2016Fithian, W. and Josse, J.Multiple Correspondence Analysis & the Multilogit Bilinear Model.
Journal of Multivariate Analysis.
pdf
slides
2016Husson, F., Josse, J. and Saporta, G.Jan de Leeuw and the French school of data analysis.
Journal of Statistical Software.
pdf
2016-2017Josse, J., Sardy, S. and Wager, S.denoiseR: a package for low rank matrix estimation.
Preprint.
pdf
Package
2016Groenen, P. and Josse, J.
Multinomial Multiple Correspondence Analysis.
Preprint.
pdf
2016Fujii, H., Josse, J., Tanioka, M., Miyachi, Y. Husson, F., and Ono, M.Regulatory T cells in melanoma revisited by a computational clustering of FOXP3+ T cell subpopulations.
Journal of Immunology.
pdf
2015Audigier, V., Husson, F. and Josse, J.MIMCA: Multiple imputation for categorical variables with multiple correspondence analysis.
Statistics and Computing.
pdf
slides
2015-2016Josse, J and Wager, S. Bootstrap-Based Regularization for Low-Rank Matrix Estimation.
Journal of Machine Learning research.
pdf
slides
2015Josse, J. and Sardy, S.Adaptive Shrinkage of singular values.
Statistics and Computing.
pdf
2015Josse, J and Husson, F.
missMDA a package to handle missing values in and with multivariate data analysis methods.
Journal of Statistical Software.
pdf
2015 Audigier, V., Husson, F. and Josse, J.Multiple Imputation with Bayesian PCA. 
Journal of Statistical Computation and Simulation.
pdf
2015-2016Josse, J. and Holmes, S.Measuring multivariate association.
Statistics Survey.
pdf
2014Audigier, V., Husson, F. and Josse, J.A principal components method to impute mixed data. 
Advances in Data analysis and Classification. 
pdf
slides
2014Josse, J., Wager, S. and Husson, F.

Confidence areas for fixed-effects PCA. 
Journal of Computational and Graphical Statistics.
pdf
slides
2014Dray, S and Josse, J.Principal component analysis with missing values: a comparative survey of methods.
Plant Ecology. 
pdf
2014Josse, J.,  van Eeuwijk, F., Piepho, H-P and Denis, J.B.Another look at Bayesian analysis of AMMI models for genotype-environment data. 
Journal of Agricultural, Biological, and Environmental Statistics.
pdf
2013Verbanck, M., Josse, J. and Husson, F.Regularized PCA to denoise and visualise data. 
Statistics and Computing.  
pdf
2013Josse, J., Timmerman, M.E. and Kiers, H.A.L.
Missing values in multi-level simultaneous component analysis.
Chemometrics and Intelligent Laboratory Systems.
pdf
2013Husson, F. and Josse, J.Handling missing values in Multiple Factor Analysis.
Food Quality and Preferences.
pdf
2013Josse, J and Husson, F.Handling missing values in exploratory multivariate data analysis methods.
Journal de la SFdS. Paper written for the best Ph.D doctoral thesis prize delivered by the French Statistical Society.
pdf
2012Josse, J., Chavent, M., Liquet, B. and Husson, F.
Regularized Iterative Multiple Correspondence Analysis.
Journal of Classification.
pdf
2011Josse, J and Husson, F.Selecting the number of components in PCA using cross-validation approximations.
Computational Statistics and Data Analysis.
pdf
2011Josse, J., Husson, F. and Pagès, J.Multiple imputation in PCA.
Advances in data analysis and classification.
pdf
2010Josse, J., Husson, F. and Pagès, J.Principal component methods - hierarchical clustering - partitional clustering: why would we need to choose for visualizing data?
Technical report.
pdf
2009Josse, J., Husson, F. and Pagès, J.Analyse en Composantes Principales.
Journal de la SFdS.
pdf
2008Josse, J., Husson, F. and Pagès, J.Testing the significance of the RV coefficient.
Computational Statistics and Data Analysis.
pdf
2008Lê S., Josse, J. and Husson, F.FactoMineR: an R package for multivariate analysis.
Journal of Statistical Software.
pdf

Software – R

I am involved in the R software community and I  have been elected as a member of the R Foundation for Statistical Computing.

Development of packages:
FactoMineR: Exploratory Data Analyses  (PCA, Multiple Correspondence Analysis, Multi-tables/view data, etc.)
missMDA:  imputation (matrix completion) of continuous and categorical data, PCA with missing values, etc.
For questions on the use of packages we have a google group
denoiseR: low rank matrix estimation with regularized SVD and bootstrap

My students have also developed R packages associated to our  works: misaem: logistic regression with missing values, mimi: Generalized low-rank models for mixed and incomplete data frames, lori: contingency table with missing values and covariates, AdaptiveConformal for adaptive conformal prediction for times series, etc.

Development of R-miss-tastic, Causal Inference Taskview.  For causal inference with missing values, you can use the R package grf  and see the pipeline to compare different estimators (IPW, DR) strategies (imputations, etc.).

Development of ICUBAM (ICU Bed Allocation Monitor) open source project to visualize the availability of resuscitation beds. This started as a personal initiative from a rescusitator in the Grand-Est region who identify the need to to visualize available Covid + beds in real time (with a respirator). ICUBAM is an operational tool for rescuscitators in times of crisis to model patient flows, anticipate bed needs and welcome patients from submerged areas. ICUBAM was deployed in 130 ICU wards in 40 départements, and inventories more than 2,000 ICU. Slides application, Slides models, paper, github.

I  served as an associate editor of Journal of Statistical Software (2011-2017) and I was a founding member of  Rforwards to leading the R community forwards in widening the participation of women and other under-represented group. I am in the R foundation conference committee and have worked for implementation of Code of Conduct.
We also created the « French R board group » to support the organization of Les Rencontres R.

Video presentation of Rforwards. Blog posts and multivariate studies of the R community. Help R by supporting with donation or through the R consortium.

Teaching

As a French professor, I taugh around 160 hours/year (lectures, computer labs mainly with the R software) and supervised master students’ projects and their internship in industry.  I was the head of a Master’s degree in Data-Science for Business at Ecole Polytechnique in collaboration with HEC business school. In addition, I give tutorials in different institutes and at conferences. Learn more. From, Sept 2020, I  taught Causal Inference in the IPP (Institut Polytechnique de Paris.) Master of Data Science at Polytechnique. For recent tutorials on missing values, see the Rmistatic plateform.

Bio

Her first employment was in the statistics department of an Agronomy University (Agrocampus Ouest) where she was trained to « the French data analysis school » and had the opportunity to work closely with researchers from various departments and increases her interest in transversal studies. In the meantime, she prepared her PhD which was defended in 2010 and rewarded by the French Statistical Society as the best PhD in applied statistics. She has specialized in missing data, visualization and the nonparametric analyses of complex data structures. Her work was rewarded by a Marie Curie European Union grant in 2013 to increase her research potential and to spend a year and a half at Stanford University. She spent a year as a researcher in INRIA before joining Ecole Polytechnique in 2016 as a Professor of Statistics. At Polytechnique, she was responsible of a Master in data-sciences for business in collaboration with HEC business school. She was a part-time visiting researcher at Google Brain Paris, for a year in 2019. In September 2020, she joined Inria as a senior researcher and created in 2022 an inria-Inserm Premedical team in data-science for health composed of clinicians and researchers in statistics, machine learning. She has published over 60 articles and written 3 books in applied statistics.  Her experience on dealing with incomplete data is recognized by the community: she organized workshops, the MissData conference, created the Rmistatic website and she is often invited to give lectures to share her experience. Her vocation is to push methodological innovation to bring useful application of her research to the user in particular in bio-sciences and health. Her current research focuses on causal inferences techniques for personalized medicine. She leads a project with the Traumabase group dedicated to the management of polytraumatized patients to help emergency doctors taking decisions. Julie Josse is dedicated to reproducible research with the R statistical software: she has developed packages including FactoMineR, denoiseR, missMDA to transfer her work, she is a member of the R foundation and of Rforwards to increase the participation of minorities in the community.

Perso: I grew up in Africa and French Polynesia. Then I arrived in Brittany a magnificent French region and I had the chance to discover Paris and now the south of France.  I am passionate about statistics but also about travelling (when I was younger on horseback) around the world. I am also fascinated by nature and science (fan of https://www.sciencefriday.com/, wildlife photographer of the year). I have a particular interest in humanitarian issues and my long-term goal is to use more of my skills for these purposes.

Interview in Academie des technologies (French, English) – Interview in MontpellierInterview in medium.

Misc

Links:
– SFdS French Statistical Society – Interested in data sciences? Join-us!!
– Some historical references on what was the French school of data analysis, data sciences.

Others projects:
Distributed computation with hospital data (with Balasubramanian Narasimhan)

Conferences organization head:
Leveraging Observational Data with Machine Learning 2021. 
Artemiss workshop at ICML 2020.
– The first MissData on missing values and matrix completion, June 2015.
– Correspondence analysis related methods CARME 2011. Videos. Let the data speak…. data analysis.
– The R conference useR! 2009.