Recent Publications

This study demonstrated the feasibility to apply causal inference techniques in incomplete observational data. DR based on a stronger …

Submitted paper co-authored with Julie Josse and the Traumabase Group.

Submitted review co-authored with Bénédicte Colnet and many others.

Objective: To assess the clinical effectiveness of oral hydroxychloroquine (HCQ) with or without azithromycin (AZI) in preventing death …

Technical report

Missing values are unavoidable when working with data. Their occurrence is exacerbated as more data from different sources become …

In Annals of Applied Statistics

MSc thesis, prepared under the supervision of Prof. René Vidal.

Recent & Upcoming Talks

More Talks

Recent Posts

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Estimate parameters of linear regression and logistic regression with missing covariates with missing data, perform model selection and …

We’re excited to announce the Leveraging Observational Data with Machine Learning Virtual Workshop - brought to you by Michael …

La neuvième journée YSP aura lieu sous un format un peut différent des éditions précédentes : deux demi-journées en distanciel les …

The Young Statisticians Europe (YSE) launch the YoungStatS blog and webinar project to promote research in Statistics and Data Science! …

I’m happy to officially share the great news that I’m a Google PhD fellow in Machine Learning this year. I’m very …



SAMSI - Missing data working group

Data integration: combining multiple data sources for causal inference & Bridging theory and practice.
Combining trial and real-world evidence studies, probability and non-probability samples, etc.


The Young Statisticians of the French Statistical Society regroups graduate students, post-docs and young professors in France.

R Forwards

R Foundation taskforce on women and other underrepresented groups.


A decision support tool for critical care management.


A unified platform on missing values, founded by the R Consortium. Have a look at Nick Tierney’s blog post introducing this project.


March 2022 – April 2022
Berkeley, CA, USA

Visiting Researcher

Simons Institute for the Theory of Computing, UC Berkeley

  • Topic: Causality research semester.
March 2020 – March 2020
Stanford, CA, USA

Visiting Student Researcher

Center for Biomedical Informatics Research, Stanford University

  • Topic: Heterogeneous treatment effect estimation.
  • Advisors: Stefan Wager, Nigam Shah.
  • Fellowship: FSMP pre-doctoral scientific research visit fellowship.
October 2018 – September 2021
Paris, France

PhD student and teaching assistant

École des Hautes Études en Sciences Sociales, École Polytechnique, Inria

  • Activities: Development of new statistical tools to analyze observational data, collaborations with clinical researchers from critical care, preparation and teaching of statistical courses in different Master’s degree programs.
  • Topic: Statistical methods for the handling of incomplete and heterogeneous data.
  • Advisors: Julie Josse, Jean-Pierre Nadal.
  • Fellowship: École doctorale de l’EHESS (ED 286) doctoral fellowship (contrat doctoral).
April 2018 – September 2018
Baltimore, MD, USA

Visiting Student Researcher

Center for Imaging Science, Johns Hopkins University

  • Topic: Global optimality of sparse dictionary learning (subject of my master’s thesis).
  • Advisor: René Vidal.
June 2017 – July 2017
Paris, France

Research Intern

LPSM, Sorbonne Université (former LSTA, Université Pierre et Marie Curie)

September 2016 – January 2017
Berlin, Germany

Working Student

Foodora GmbH

Data analyses, mainly based on Google Analytics, for Business Intelligence and Global Logistics Operations Teams. Supervisor: Tobias Schmidt
July 2016 – August 2016
Berlin, Germany

Data Science Intern

Foodora GmbH

Data analyses, mainly based on Google Analytics, for Business Intelligence and Global Logistics Operations Teams. Supervisor: Tobias Schmidt



  • Causal inference for observational clinical data (AI4Health winter school).
    Tutorial on missing values and practical sessions.


  • MAP535 (École Polytechnique, 3ème année/Master 1, Applied mathematics, Regression).
    Teaching assistant (tutorials and practical sessions).
  • MAP573 (École Polytechnique, 3ème année/Master 1, Applied mathematics, Data Analysis and Unsupervised Learning).
    Student research project advisor.
  • MAP670M (École Polytechnique, Master 2, Data Science and Mathematics for Life Sciences, Missing data and causality).
    Teaching assistant (tutorials and practical sessions).


  • MAP573 (École Polytechnique, 3ème année/Master 1, Applied mathematics, R for statistics).
    Student research project advisor.
  • D4M Certificate (HEC, Master 2, Management, Case study).
    Teaching assistant (practical sessions).


My CV is available in English, French and German.


  • Institut für Public Health, Berlin