A quick resume

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

Education

• 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

• In organizing committee : Lifting Inference with Kernel Embeddings 2023 June 26-30, 2023.
• Main organizer of the workshop : Current frontiers in Gaussian Processes Aug. 24-26, 2022.
• In organizing committee and webmaster : Lifting Inference with Kernel Embeddings 2022 Jan. 10-14, 2022.
• Reviewer for Artificial Intelligence and Statistics (AISTATS) 2022, 2023
• Reviewer for the workshop : Machine Learning and the Physical Sciences at NeurIPS 2021, 2022.
• Reviewer for the workshop: Synergy of Scientific and Machine Learning Modeling at ICML 2023.