EDUCATION

• Ph.D. in Computer Science | Inria Paris & École normale supérieure - PSL University, VALDA team. Supervised by Paul Boniol and Michael Thomazo. 2024 - Now
  Segmentation, interpretability, and representation in (multivariate) time series.
• M.Sc. in Computer Science (Research track) | National University of Singapore. 2022 - 2024
  Neural Networks, Deep Learning, Data Mining, Knowledge Discovery, NLP, Trustworthy ML.
  Thesis: Data-Driven Discovery of State Variables from Dynamical System Observations.
• M.Sc. in Applied Mathematics (Grande École Engineering Degree) | ENSTA Paris - Institut Polytechnique de Paris. 2019 - 2022
  Optimization, Statistics, Probabilities, PDEs, Scientific Programming, Signal Processing, Databases, Time Series.
  M1 Thesis: Neural Networks for Turbulence Modeling, Harvard University.
• B.Sc. in Applied Mathematics | Université de Toulouse. 2016 - 2019
  Calculus, Algebra, Topology, Probabilities, Numerical Methods, Stochastic Simulations.

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EXPERIENCE

• Okinawa Institute of Science and Technology | Development of interpretable methods for time series segmentation, supervised by Prof. Makoto Yamada at OIST - Machine Learning and Data Science unit. Japan, June - August 2025
• European Space Agency | Analysis of ESA Climate Change Initiative contribution to IPCC climate science reports. Harwell, UK, February - May 2024
  > Private report, but analysis code and results are available on GitHub
• CNRS | Physics-informed neural networks for dynamical systems at CNRS@CREATE (Centre National de la Recherche Scientifique) in Singapore, under the supervision of Stéphane Bressan, September 2022 to November 2023
  > Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems, NeurIPS'24 (ML4PS)
  > Physics-informed Discovery of State Variables in Second-Order and Hamiltonian Systems, ACIIDS'25
  > Assessing the Effectiveness of Intrinsic Dimension Estimators for Uncovering the Phase Space Dimensionality of Dynamical Systems from State Observations, DEXA'23
• IPCC | Meta-analysis of the 6th Assessment Report of the IPCC (Intergovernmental Panel on Climate Change), under the supervision of Sarah Connors, September 2021 to February 2022
  > What 13,500 citations reveal about the IPCC’s climate science report, Carbon Brief
  > Analysis of the WGI contribution to the Sixth Assessment Report: Review of the WGI AR6 references, IPCC
  > Textual analysis of the WGI contribution to the AR6, IPCC
• Harvard University | Physics-informed neural networks for Navier-Stokes equations in turbulent channel flow, within the StellarDNN team at John A. Paulson School of Engineering and Applied Sciences (SEAS), under the supervision of David Sondak and Pavlos Protopapas, May - August 2021
  > Solving Reynolds-Averaged Navier-Stokes equations in Turbulent Channel Flow
• IRIT | Machine learning for predictive maintenance of aircraft engines within the SAMoVA team of IRIT (Toulouse Institute for Research in Computer Science) in collaboration with ISAE Supaéro, under the supervision of Thomas Pellegrini, June 2019
  > Data driven predictive maintenance of aircraft engines
• Météo France | Statistical modeling applied to visibility and fog phenomena at the Forecasting Operations Department, under the supervision of Olivier Mestre, August 2018