Curriculum

Experience

  • 2023 (5 months)
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    Graduate Trainee in Cosmology
    Institut d'Astrophysique de Paris, France
    • Trainee in the "Large-scale structure and distant Universe" group.
    • I work with the Aquila Consortium on novel simulation-based inference techniques to extract cosmological information from astronomical data, with future application to Euclid data.
  • 2022 - 2023 (1 year)
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    Master thesis in Fluid Dynamics
    ONERA - The French Aerospace Lab, Châtillon, France
    • Trainee in the NFLU (Digital methods for fluid dynamics) and MSAT (Advanced turbulence modelling and simulation) teams.
    • I developed a novel confinement method to better preserve vortical structures in direct numerical simulations of turbulent flows.
  • 2022
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    Master's degree in Mathematics and Hybrid AI
    Université de Toulouse, France
    • Double Master's degree in Mathematics and Hybrid AI, with a strong emphasis on mathematical modelling.
    • Prepared at INSA Toulouse and ENSEEIHT (details below).
  • 2021 (2 months)
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    Research Trainee
    CRG - Centro de Regulación Genómica, Barcelona, Spain
    • Summer traineeship in Guigo's Lab.
    • I implemented a scalable pipeline for automatic identification of IR-QTL (Intron Retention Quantitative Trait Loci) based on SVA (Surrogate Variable Analysis).
  • 2020 - 2022
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    Junior Bioinformatics Scientist
    INSERM (French National Institute of Health and Medical Research), Toulouse, France
    • I studied machine learning methods to predict enhancer-gene relations in the human genome ; in parallel to my Master in Mathematical Modelling.
  • 2020 (2 months)
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    Research Trainee
    IMT (Toulouse Mathematics Institute)
    • I studied the convergence speed of series of quantum non-demolition measurements (QND measurements).
  • 2020 (4 months)
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    Research Trainee
    IRAP - Institut de Recherche en Astrophysique et Planétologie, Toulouse, France
    • I studied kinetic scale plasma turbulence in the solar wind.
  • 2019 (3 months)
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    Artificial Intelligence Trainee
    Inria (French National Institute for Research in Computer Science and Automation), MIMESIS team
    • I analysed the robustness of a deep learning method for data-driven bio-mechanical simulation, with respect to data sparsity and noises.
    • As a result, we implemented an efficient transfer learning solution to learn new parameters with few data.
  • july 2017
    Farm hand
    La Ferme aux 100 Blés, Saint-Broing-Les-Moines, France

Education

  • 2022
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    MS in Mathematical Modelling
    INSA Toulouse, France
    • Double Master's degree in Mathematics and Hybrid AI with ENSEEIHT
    • This is a transversal curriculum built around 3 major axes
      • numerical mathematical modelling
      • statistical mathematical modelling
      • artificial intelligence
  • 2022
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    MS in Artificial Intelligence
    INP-ENSEEIHT, France
    • Double Master's degree (Diplôme d’ingénieur - Master of Science in French Grande Ecoles) in Mathematics and Hybrid AI together with INSA Toulouse.
  • 2020
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    BS in Fundamental Physics and Internship in Plasma Physics
    Paris-Saclay University, Gif-sur-Yvette, France
    • I took a full year-off with respect to my training at INSA Toulouse, in order to develop my knowledge of fundamental physics.
    • I was enrolled in the Magistère de Physique Fondamentale d’Orsay, where I studied for one semester, and then completed my gap year with 2 traineeships in theoretical physics.
  • 2019
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    BS in Mathematics and Basic Sciences
    INSA Toulouse, France
    • 3-year preparatory cycle (Bachelor's degree) in Fundamental and Applied sciences, with a strong emphasis on mathematics. Those 3 years are part of a selective 5-year curriculum leading to a Diplôme d’ingénieur (Master of Science in French Grande Ecoles).
    • Scholarship holder (level 6/7)
  • 2016
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    High School Diploma in Science (Baccalauréat Général Scientifique)
    Lycée Charles-Emiles Freppel, Obernai, France
    • With highest honors (18.1/20)

Professional projects

  • 2022-early 2023
    Development of a novel confinement method to better preserve vorticity in direct numerical simulations of turbulent flows
    • More to come...
  • 2020-2022
    Genome-wide identification of E/G interactions
    • If the identification of all enhancers present in a given cell type is not a solved problem, the identification of the relationships between enhancers and genes, i.e. which genes are the targets of which enhancers, in a particular cell type is even more complex. In this project, we investigate two of the most recent methods in the field.

Academic Interests

  • Mathematics
    • optimization, optimal control theory, data assimilation, differential equations
    • Bayesian and simulation-based inference, physics-informed neural networks, etc
  • Physics
    • Cosmology
    • Simulation for astrophysics, plasma physics, fluid dynamics, etc

Other Interests

  • Hobbies: Photography, martial arts, running, hiking, bouldering