Julie Josse – Julie josse

I am a Senior Researcher at Inria (National research center in digital science) and the head of the Inria-Inserm (National research center in health) team PreMeDICaL (precision medicine by data integration and causal learning). Member of Idesp (Inserm – Um).
News: Talk at Online Causal Inference Seminar on leveraging incomplete RCT and observational data; Talk (short intro on causal inference) at Exposome Montpellier.
– Talk at celebrating causal inference in Medicine and Public Health
– Talk Simons Institute: causal inference from Brain Trauma. slides.
– Talks: Supervised learning with missing values. Imputation and regress: slides, videos Banff; Pres of linear models, random forest, neural network: slides, videos
– Tutorials: AutoML2022 ICML slides, video. A missing values tour: 2022 slides les diableret PhD school, 2019 useR slides, 2019 video (start at 30′)
– Interns/Phd/Postdoc positions. Contact me.

– Project TrauMatrix decision tool for intensive Care; short Video, Podcast
– ICUBAM ICU Bed Availability Monitoring (Grand Est région) during the COVID-19 epidemic. Fork. slides.
– R-mis-static website, 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, 226, 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), 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

Projects and collaboration:
Causal inference:
– Causal inference with missing values (with Stefan Wager)
– Transporting causal effect, combining RCT and observational data (with Shu Yang),
– Survival causal inference, sensitivity analyses, policy learning (with Antoine Chambaz)
Missing values:
– Missing Non At Random data (with Claire Boyer)
– Supervised Learning with missing values: Random Forests, Neural Nets (with Erwan Scornet, Gael Varoquaux and Marine Le Morvan)
– Variable selection to control the FDR with missing values (with Gosia Bogdan)
– PCA with missing values, multiple imputation, package missMDA (with François Husson)
Health applications:
– Handling severe trauma patients, with the Traumabase group, J.P Nadal and Capgemini
– Covid19: Application for bed allocation monitoring/Predict need of intubation/ Effect of hydrochloroquine
Others:
– New collaborations: with Jes Frellsen with a grant
– Distributed computation with hospital data (with Balasubramanian Narasimhan)
– Exploratory data analysis (What was the French school of data analysis?)

Students & group’s meeting: the missing data and causal inference group at Inria

Associate Editor: Past: Journal of Computational & Graphical Statistics. Journal of Statistical Software. (7 years). AC for Neurips, ICLR.

An overview of my research up to 2016 can be found in my Habilitation. (slides)

Publications:

Year	Authors	Title	link
2023	Zaffran, Josse, J. M., Dieuleveut A., Romano, Y.	Conformal prediction with missing values. Submitted.	pdf
2022	Colnet, B, Josse, J., Varoquaux, G., Scornet, E.	Reweighting the RCT for generalization: finite sample analysis and variable selection. Submitted.	pdf
2022	Blet et al.	Association between in-ICU red blood cells transfusion and one-year mortality in ICU survivors. Critical Care.	pdf
2022	Colnet, B, Josse, J., Varoquaux, G., Scornet, E.	Generalizing a causal effect: sensitivity analysis and missing covariates. Journal of Causal Inference.	pdf slides
2022	Gauss et al.	Is Early Norepinephrine Associated with 24-hour Mortality of Blunt Trauma Patients in Haemorrhagic Shock? An International Cohort Study. Jama Network.	pdf
2022	Garaix et al.	Decision-making tools for healthcare structures in times of pandemic. Anaesthesia Critical Care & Pain Medicine.	pdf
2022	Zaffran et al.	Adaptive conformal prediction for time series. ICML2022.	pdf slides video
2022	Perez-Lebel et al.	Benchmarking missing-values approaches for predictive models on health databases. GigaScience.	pdf
2021	Le Morvan, J. Josse, E. Scornet. & G. Varoquaux	What’s a good imputation to predict with missing values? Neurips 2021. (Spotlight).	pdf video slides
2021	Sportisse, A. et al.	Model-based Clustering with Missing Not At Random Data. Submitted.	pdf
2021	Mayer, I., Josse, J & Traumbase	Transporting treatment effects with incomplete attributes. Biometrical Journal	pdf
2020-2023	Colnet, B et al.	Causal inference methods for combining randomized trials and observational studies: a review. Statistical Science.	pdf
2020	Le Morvan, J. Josse, M., Moreaux, T, E. Scornet. & G. Varoquaux	Neumiss networks: differential programming for supervised learning with missing values. Neurips2020. (Oral)	pdf slides video
2020	Sbidian 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
2020	Consortium ICUBAM	ICU Bed Availability Monitoring and analysis in the Grand Est région of France during the COVID-19 epidemic. Statistiques et Société.	pdf slides
2020	A. 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
2020	A. Sportisse, C. Boyer, A. Dieuleveut, J. Josse.	Debiasing Stochastic Gradient Descent to handle missing values. Neurips2020.	pdf slides
2020	J.D. Moyer et al.	Trauma reloaded: Trauma registry in the era of data science. Anaesthesia Critical Care & Pain Medicine.	pdf
2020	Muzellec, B., Josse, J. Boyer, C. & Cuturi, M.	Missing Data Imputation using Optimal Transport. ICML2020.	pdf slides videos code
2019	Josse, J., Mayer, I, & Vert, J.P.	MissDeepCausal: causal inference from incomplete data using deep latent variable models. Preprint.	pdf
2020	Le Morvan, M., N. Prost, J. Josse, E. Scornet. & G. Varoquaux	Linear predictor on linearly-generated data with missing values: non consistency and solutions. AISTAT2020.	pdf slides
2020	Descloux, 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
2022	Mayer, I, Sportisse, A., Josse, J., Vialaneix, N., Tierney, N.	R-miss-tastic: a unified platform for missing values methods and workflows. R journal.	pdf
2019-20	Mayer, 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
2019-21	M. 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
2019	Josse, J., Prost, N., Scornet, E. & Varoquaux, G.	On the consistency of supervised learning with missing values. Preprint.	pdf slides code slides
2019	G. 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
2019	Hamada, S et al.	Effect of Fibrinogen administration on early mortality in traumatic haemorrhagic shock: a propensity score analysis. Journal of Trauma.
2019	Sportisse, A., Boyer, C. and Josse, J.	Low-rank estimation with missing non at random data. Statistics and Computing.	pdf code
2018	Josse, J., Husson, F. Robin, G. and Balasubramanian. N.	Imputation of mixed data with multilevel SVD. Journal of Computational and Graphical Statistics.	pdf slides
2018	Robin, 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
2018	Jiang, 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. Moulines	Low-rank interactions and sparse additive effects model for large data frames. NeurIPS 2018.
2018	Josse, J. and Reiter, J.	Introduction to the Special Section on Missing Data. Statistical Sciences.	pdf
2018	Seijo-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.
2017-2018	Mozharovskyi, P., Husson, F. and Josse, J.	Nonparametric imputation by data depth. Journal of the American Statistical Association.	pdf slides code
2017	Holmes, S and Josse, J.	50 years of data-sciences, discussion. Journal of Computational and Graphical Statistics.	pdf
2017	Bollmann, 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
2017	G. 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
2017	Foulley, JL, Celeux, G and Josse, J.	Empirical Bayes approaches to PageRank type algorithms for rating scientific journals. Technical report.	pdf slides
2016	Sobczyk, P, Bogdan, M. and Josse, J.	PCA using penalized semi-integrated likelihood. Journal of Computational and Graphical Statistics.	pdf
2016	Fithian, W. and Josse, J.	Multiple Correspondence Analysis & the Multilogit Bilinear Model. Journal of Multivariate Analysis.	pdf slides
2016	Husson, F., Josse, J. and Saporta, G.	Jan de Leeuw and the French school of data analysis. Journal of Statistical Software.	pdf
2016-2017	Josse, J., Sardy, S. and Wager, S.	denoiseR: a package for low rank matrix estimation. Preprint.	pdf Package
2016	Groenen, P. and Josse, J.	Multinomial Multiple Correspondence Analysis. Preprint.	pdf
2016	Fujii, 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
2015	Audigier, V., Husson, F. and Josse, J.	MIMCA: Multiple imputation for categorical variables with multiple correspondence analysis. Statistics and Computing.	pdf slides
2015-2016	Josse, J and Wager, S.	Bootstrap-Based Regularization for Low-Rank Matrix Estimation. Journal of Machine Learning research.	pdf slides
2015	Josse, J. and Sardy, S.	Adaptive Shrinkage of singular values. Statistics and Computing.	pdf
2015	Josse, 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
2015-2016	Josse, J. and Holmes, S.	Measuring multivariate association. Statistics Survey.	pdf
2014	Audigier, V., Husson, F. and Josse, J.	A principal components method to impute mixed data. Advances in Data analysis and Classification.	pdf slides
2014	Josse, J., Wager, S. and Husson, F.	Confidence areas for fixed-effects PCA. Journal of Computational and Graphical Statistics.	pdf slides
2014	Dray, S and Josse, J.	Principal component analysis with missing values: a comparative survey of methods. Plant Ecology.	pdf
2014	Josse, 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
2013	Verbanck, M., Josse, J. and Husson, F.	Regularized PCA to denoise and visualise data. Statistics and Computing.	pdf
2013	Josse, J., Timmerman, M.E. and Kiers, H.A.L.	Missing values in multi-level simultaneous component analysis. Chemometrics and Intelligent Laboratory Systems.	pdf
2013	Husson, F. and Josse, J.	Handling missing values in Multiple Factor Analysis. Food Quality and Preferences.	pdf
2013	Josse, 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
2012	Josse, J., Chavent, M., Liquet, B. and Husson, F.	Regularized Iterative Multiple Correspondence Analysis. Journal of Classification.	pdf
2011	Josse, J and Husson, F.	Selecting the number of components in PCA using cross-validation approximations. Computational Statistics and Data Analysis.	pdf
2011	Josse, J., Husson, F. and Pagès, J.	Multiple imputation in PCA. Advances in data analysis and classification.	pdf
2010	Josse, 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
2009	Josse, J., Husson, F. and Pagès, J.	Analyse en Composantes Principales. Journal de la SFdS.	pdf
2008	Josse, J., Husson, F. and Pagès, J.	Testing the significance of the RV coefficient. Computational Statistics and Data Analysis.	pdf
2008	Lê S., Josse, J. and Husson, F.	FactoMineR: an R package for multivariate analysis. Journal of Statistical Software.	pdf

Books

R pour les statistiques et la science des données 2018.
Cornillon, Guyader, Husson, Jégou, Josse, Kloareg, Matzner-Løber, Rouvière. (2008, 2010, 2012). Statistique avec R. PUR.
Cornillon, Guyader, Husson, Jégou, Josse, Kloareg, Matzner-Løber, Rouvière. (2012). R for statistics. Chapman & Hall.
Chapter in Books: Husson, F. & Josse, J. (2014). Multiple correspondence analysis. The Visualization and Verbalization of Data

Software – R

I am involved in the R software community and I am sincerely glad to have been elected as a member of the R Foundation for Statistical Computing. Please if R is helping you, help us by supporting with donation

Development of packages:
FactoMineR: visualization with principal components methods
missMDA: missing values (imputation continuous, categorical data) – matrix completion
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

Development of R-miss-tastic:
Project funded by the R consortium (Infrastructure Steering Committee) to federate the community. Aim: a reference platform on the theme of missing data management (list existing packages, available literature, tutorials, analysis workflows on data, main actors, etc)

Causal Inference Taskview

If you want to do causal inference with missing values, you can use the R package grf where a double robust method handling missing covariates is implemented and see the pipeline to compare different estimators (IPW, DR) strategies (imputations, etc.).

Development of ICUBAM (ICU Bed Allocation Monitor) as an open source project with Inria, 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 has been 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 am involved in 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 work for implementation of Code of Conduct.
With M. Chavent, S. Dray, R. Genuer, F.Husson, B. Liquet, J. Sarracco, we created the « French R board group » to support the organization of Les Rencontres R.

News: Video presentation of Rforwards. Blog posts and multivariate studies of the R community.
Support R with 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 addition, I give tutorials in different institutes and at conferences. Learn more. From, Sept 2020, I taugh 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 other 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 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. She has been a visiting researcher at Google Brain Paris, for a year (2 days a week) 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. 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 making 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 (often 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 Montpellier – Interview in medium.

Misc

Links:
– SFdS French Statistical Society – Interested in data sciences? Join-us!!
– Some historical references on french data analysis, data sciences.
– Roger Peng’s activities and Brian Caffo youtube channel.

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.

Content