You can download my CV by clicking on this sentence, or read a summary below.


  • Ph.D. in statistics, University of Bern (Bern, Switzerland), Nov. 2018 - June 2023
  • M.S. in Applied Mathematics, University Paris Dauphine (Paris, France), 2017 - 2018
  • M.Eng. in Data Sciences and Statistics, Ecole des Mines de Saint-Etienne, 2015 - 2018
  • Preparatory classes, Lycée Henri IV (Paris, France), 2013 - 2015

Work experience

  • Research and Teaching assistant: Nov. 2018 - Actual
    At the institute of statistics and actuarial sciences, University of Bern (Bern, Switzerland).
    Until May 2020, the affiliation was shared with Idiap Research Institute (Martigny, Switzerland).
    • Research assistant with a strong focus on the Ph.D. topic. Active participation in scientific collaborations, proficiency in coding and creating reproducible examples using R language. Main research interests: Uncertainty Quantification, Gaussian Processes, Bayesian Optimization, Bayesian Statistics, Computer simulation models.
    • Senior consultant of the institute of statistics, University of Bern. Helping academics and companies planning and conducting statistical analysis (Jan. 2021 - Dec. 2023)

    • Teaching assistant for the institute of statistics at University of Bern:
      • R course (Fall term 2022).
      • Linear models I (Fall term 2022).
      • Spatial statistics (Spring term 2022).
      • Statistics for climate sciences I/II (Fall term 2019, Spring term 2020, Fall term 2020).
      • Optimization methods (Spring term 2019, Spring term 2023).
    • Co-supervisor (main supervisor: D. Ginsbourger) of the Master thesis: “Gaussian process regression on molecules: some performance assessments and comparisons”. (2021)
    • Teaching assistant in the continuing education program Master AI at Idiap Research Institute and Unidistance.
      • Foundations in statistics for AI (Spring term 2019 and 2020).
  • Research Intern: Mar. - Oct. 2018.
    At Idiap Research Institute (Martigny, Switzerland)
    • Working on “Statistical and machine learning approaches to optimization problems under uncertainty arising in energy planning”, under the supervision of David Ginsbourger.
  • Engineering Intern: Summer 2017
    • ELM Leblanc SAS (Drancy, France)
    • Working on “data integration for smart energetic systems”: identifying exploitable data, possible applications and inherent risks.

Awards or distinctions

  • MASCOT-NUM 2022 annual meeting: Award for the best oral presentation.
  • University of Bern’s Fund for the Promotion of Young Researchers : Maximum grant award of CHF 5’000 to organize a workshop.
  • MASCOT-NUM 2021 annual meeting: Award for the best poster presentation.
  • SIAM (Society for Industrial and Applied Mathematics): Travel award for SIAM UQ 2022.

Academical service