Curriculum Vitae - January 2021 Julie Josse Nationality: French. Date of birth: 10th November 1983. E-mail: [email protected] Web: http://juliejosse.com Position Sept 2020 - Advanced Researcher, INRIA. Dec 2020 - Scientific collaborator + Teaching, CMAP Polytechnique. Working Address Inria, Antenne de Montpellier, 860 rue Saint Priest, 34000 Montpellier. Education - previous positions • 2016 - August 2020. Professor of Statistics Ecole Polytechnique (IPP) - CMAP. Member of INRIA Team XPOP. • April 2019 - September 2020. Visiting Researcher, 2 days a week. Google Brain IA, Paris. • 2016: Habilitation à diriger des recherches defended 30 August. Orsay University. “Con- tribution to missing values and principal components methods". Committee: reviewers: G. Celeux (INRIA), J. Friedman (Univ. Stanford), P. Hoff (Univ. Washington); members: A. Ruiz-Gazen (Univ. Toulouse 1), P. Massart (Orsay), E. Moulines (Polytechnique), F. Murtagh (Univ. London), D. Paindaveine (Univ. Bruxelles). • 2016. Research associate (délégation) team SELECT, INRIA University Paris-Sud, Orsay • 2011–2015 Associate professor - Statistics department, Agrocampus Ouest (grande école, Ministry of agriculture), Rennes (Brittany, France). (IRMAR - UMR 6625 - CNRS). • 2007–2010: PhD thesis in Statistics defended 22 October 2010. Agrocampus. “Gestion des données manquantes en analyse exploratoire des données”. Advisors: J. Pages & F. Husson (Pr, Agrocampus). Committee: M. Greenacre (Pr, Barcelona Spain), H.A.L. Kiers (Pr, Groeningen the Netherlands, reviewer), G. Govaert (Pr, Univ. Compiegne France, reviewer), P. Kroonenberg (Pr, Leiden the Netherlands), A. Morin (Ass-pr, IRISA, Rennes France). Award: best doctoral PhD thesis in applied statistics by the French Statistical Society (Prix Marie- Jeanne Laurent-Duhamel). • 2006–2010. Engineer - Ingénieur d’études (computer manager) - Agrocampus. • 2004–2006 Master degree Statistics summa cum laude University Rennes II, France. Publications Articles 1. Colnet, B., J. Josse, E. Scornet & G. Varoquaux. (2021). Generalizing a causal effect: sensitivity analysis and missing covariates. 2. Mayer, I., J. Josse & the traumabase group (2021). Transporting treatment effects with incomplete attributes. Submitted. 3. P. Sobczyk et al. VARCLUST: clustering variables using dimensionality reduction. Submitted. 4. Moyer, JD, et al. (2020). Trauma reloaded: Trauma registry in the era of data science Anaesthesia. Critical Care & Pain Medicine. 5. Le Morvan, M., J. Josse, T. Moreaux, E. Scornet. and G. Varoquaux. (2020). NeuMiss networks: networks: differential programming forsupervised learning with missing values. Neurips2020. 6. Sbidian et al. (2020). 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. 7. Consortium ICUBAM (2020). ICU Bed Availability Monitoring and analysis in the Grand Est région of France during the COVID-19 epidemic. 8. Debiasing Stochastic Gradient Descent to handle missing values.(2020). Sportisse, A., Josse, J. , Boyer, C. and Dieuleveut, A. Neurips2020. 9. Sportisse, A., Boyer, C. and Josse, J. (2020). Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data. Neurips2020. 10. Le Morvan, M., N. Prost, J. Josse, E. Scornet. and G. Varoquaux. (2020). Linear predictor on linearly generated data with missing values: nonconsistency and solutions. AISTAT2020. 11. Mayer, I, Josse, J., Wager, S., Sverdr, E., Moyer, J.D. and Gauss, T. Doubly robust treatment effect estimation with incomplete confounders. 2020. Annals Of Applied Statistics. 12. Missing Data Imputation using Optimal Transport. Muzellec, B. Josse, J. , Boyer, C. and Cuturi, M. International Conference on Machine Learning ICML2020. 13. Josse, J., Mayer, I, & Vert, J.P. MissDeepCausal: causal inference from incomplete data using deep latent variable models. 14. Descloux, P. Boyer, C., Josse, J. Sportisse, A. and Sardy, S. Robust Lasso-Zero for sparse corruption and model selection with missing covariates. Submitted. 15. Mayer, I, Josse, J., Vialaneix, N., Tierney, N. R-miss-tastic: a unified platform for missing values methods and workfows. 2019. Submitted. 16. 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. 17. Josse, J, Prost, N., Scornet, E. & Varoquaux, G. On the consistency of supervised learning with missing values. In revision in Journal of Machine Learning Research (JMLR). 18. Sportissse, A., Boyer, C. and Josse, J. (2019). Imputation and low-rank estimation with Missing Non At Random data. Statistics & Computing. 19. G. Robin, O. Klopp, J. Josse, E. Moulines, and R. Tibshirani (2019). Main effects and interactions in mixed and incomplete data frames. Journal of American Statistical Association. 20. Jiang, W., Lavielle, M. and Josse, J. (2018). Logistic Regression with Missing Covariates: Parameter Estimation, Model Selection and Prediction within a Joint-Modeling Framework. Computational Statistics & Data Analysis. 21. G. Robin, Hoi To Wai, J. Josse, O. Klopp and E. Moulines. (2018). Low-rank interactions and sparse additive effects model for large data frames. NeurIPS. 22. Josse, J., Husson, F. Robin, G. and Balasubramanian, N (2018). Imputation of mixed data with multilevel SVD. Journal of Computational and Graphical Statistics. 23. Mozharovskyi, P., Husson, F. & Josse, J. (2018). Nonparametric imputation by data depth. Journal of the American Statistical Association. 24. Hamada et al. (2018). Effect of Fibrinogen administration on early mortality in traumatic haemor- rhagic shock: a propensity score analysis. Journal of Trauma. 25. Seijo-Pardo, B., Alonso-Betanzos, A., P. Bennett, K. Bolón-Canedo, Josse, J., Saeed, M., Guyon, I. (2018). Feature selection in the presence of missing data. Neurocomputing, ESANN. 26. Josse, J. & Reiter, J. (2018). Introduction to the Special Section on Missing Data. Journal of Statistical Sciences. 27. Robin, G, Sardy, S., Moulines, E. and Josse, J. ( 2018). Low rank log-linear models for contingency tables. Journal of Multivariate Analysis. 28. Josse, J. & Holmes, S. (2017). Discussion of 50 Years of Data Science. Journal of Computational and Graphical Statistics. 29. Bollmann, S., Cook, Di. Dumas, J., Fox, J., Josse, J., Keyes, O. Strobl, C., Turner, H. & Debelak, R. (2017). A First Survey on the Diversity of the R Community. R journal. 30. Josse, J. , Marin, J.M. & Robert, C.P. Some discussions on the Read Paper "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig. 31. Sobczyk, P., Bogdan, M. & Josse, J. (2017). Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood. Journal of Computational and Graphical Statistics. 32. Foulley, JL, Celeux, G and Josse, J. Empirical Bayes approaches to PageRank type algorithms for rating scientific journals. Preprint. 33. Fithian, W. & Josse, J. (2016). Multiple Correspondence Analysis & the Multilogit Bilinear Model. Journal of Multivariate Analysis. 34. Husson, F., Josse, J. & Saporta, G. (2016). Jan de Leeuw and the French school of data analysis. Journal of Statistical Software. 35. Groenen, P. & Josse, J. (2016). Multinomial multiple correspondence analysis. On Arxiv: http://arxiv.org/abs/1603.03174. 36. Josse, J., Sardy, S. & Wager, S. (2016). denoiseR a package for low rank-matrix estimation. On Arxiv: http://arxiv-web3.library.cornell.edu/abs/1310.6602, Journal of Statistical Software. 37. Fujii H., Josse J., Tanioka M., Miyachi Y., Husson F. and Ono M. (2016). Regulatory T cells in melanoma revisited by a computational clustering of FOXP3+ T cell subpopulations. Journal of Immunology. 38. Josse, J. & Wager, S. (2016). Stable Autoencoding: A Flexible Framework for Regularized Low-Rank Matrix Estimation. Journal of Machine Learning Research. 39. Audigier, V., Husson, F. & Josse, J. (2016) MIMCA: Multiple imputation for categorical variables with multiple correspondence analysis. Statistics and Computing. 40. Josse, J. & Holmes, S (2015). Tests of independence and Beyond. Statistics Survey. 41. Audigier, V., Josse, J. & Husson, F. (2015). Multiple imputation for continuous variable using Bayesian PCA. Journal of Statistical Computation and Simulation. 42. Josse, J. & Sardy, S. (2015). Adaptive Shrinkage of singular values. Statistics and Computing. 43. Josse, J. & Husson, F. (2015). missMDA a package to handle missing values in and with multivariate data analysis methods. Journal of Statistical Software. 44. Audigier, V., Husson, F. & Josse, J. (2014). A principal components method to impute mixed data. Advances in Data Analysis and Classification. 45. Josse, J., Wager, S. & Husson, F. (2014). Confidence areas for fixed-effects PCA. Journal of Compu- tational and Graphical Statistics. 46. Dray, S & Josse, J. (2014). Principal component analysis with missing values: a comparative survey of methods. Plant Ecology. 216 (5), 657-667. 47. Josse, J., van Eeuwijk, F., Piepho, H-P, Denis, J.B. (2014). Another look at Bayesian analysis of AMMI models for genotype-environment data. Journal of Agricultural, Biological, and Environmental Statistics. 19 (2), 240-257 48. Verbanck, M. & Josse, J. & Husson, F. (2015). Regularized PCA to denoise and visualise data. Statistics and Computing. 25 (2), 471-486. 49. Josse, J. & Timmerman, M.E. & Kiers, H.A.L. (2013). Missing values in multi-level simultaneous component analysis.