CV

Education

PhD in applied mathematics and scientific computing, Université de Bordeaux, 2020
MS in applied mathematics and scientific computing, Ensimag - Grenoble INP, 2017
BS in mathematics, Université de Bordeaux, 2015

Work experience

Postdoctoral researcher (Dec. 2023 - Today)
École Polytechnique Fédérale de Lausanne (Switzerland)
Research topics:
– Dynamical low-rank approximations for data assimilation
– Reduced-order and machine learning surrogates for turbulent flows
Postdoctoral advisors: Prof. Fabio Nobile and Prof. Jürg A. Schiffmann
Postdoctoral researcher (Nov. 2021 - Nov. 2023)
Centre Inria de Paris (France)
Research topic: Low-rank methods for inverse problems and uncertainty quantification
Postdoctoral advisors: Damiano Lombardi and Miguel A. Fernández
Postdoctoral researcher (Apr. 2021 - Oct. 2021)
Centre Inria de Bordeaux (France)
Research topic: Graph-based methods for geometry extraction of abdominal aortic aneurysms
Postdoctoral advisor: Prof. Angelo Iollo
Scientific stay (Jan. 2020 - Apr. 2020)
Stanford University (USA)
Research topic: Space-local reduced-order modeling for compressible flows
Collaborators: Prof. Angelo Iollo and Prof. Charbel Farhat
Doctoral student (Oct. 2017 - Nov. 2020)
Université de Bordeaux (France)
Research topic: Projection-based reduced-order models for transport-dominated and kinetic equations
PhD supervisor: Prof. Angelo Iollo

Skills

Programming languages: Maple, Matlab, Python, Julia, Fortran, C++.
Software and libraries: FreeFem++, OpenMP, MPI, ScaLAPACK, PETSc, OpenFOAM.

Publications

Dynamical Low-Rank Ensemble Kalman filter for State/Parameter estimation

F. Nobile, S. Riffaud, T. T. Trindade, "Dynamical Low-Rank Ensemble Kalman filter for State/Parameter estimation", arXiv preprint arXiv:2602.06614 (2026).

Robust high-order low-rank BUG integrators based on explicit Runge-Kutta methods

F. Nobile, S. Riffaud, "Robust high-order low-rank BUG integrators based on explicit Runge-Kutta methods", arXiv preprint arXiv:2502.07040 (2025).

Preconditioners for multilinear problems arising in parametric Partial Differential Equations

D. Lombardi, S. Riffaud, "Preconditioners for multilinear problems arising in parametric Partial Differential Equations", Numerical Linear Algebra with Applications (2025), vol.32, no 5, p. e70037.

Accurate and robust predictions for model order reduction via an adaptive, hybrid FOM/ROM approach

S. Riffaud, "Accurate and robust predictions for model order reduction via an adaptive, hybrid FOM/ROM approach", Journal of Computational Physics 523 (2025), p. 113677.

A low-rank solver for parameter estimation and uncertainty quantification in linear time dependent systems of Partial Differential Equations

S. Riffaud, M. A. Fernández, D. Lombardi, "A low-rank solver for parameter estimation and uncertainty quantification in time-dependent systems of Partial Differential Equations", Journal of Scientific Computing 99 (2024), no 2, p. 34.

Automatic branch detection of the arterial system from abdominal aortic segmentation

S. Riffaud, G. Ravon, T. Allard, F. Bernard, A. Iollo, C. Caradu, "Automatic branch detection of the arterial system from abdominal aortic segmentation", Medical & Biological Engineering & Computing 60 (2022), no 9, p. 2639–2654.

The DGDD method for reduced-order modeling of conservation laws

S. Riffaud, M. Bergmann, C. Farhat, S. Grimberg, A. Iollo, "The DGDD method for the reduced-order modeling of conservation laws", Journal of Computational Physics 437 (2021), p. 110336.

ROMs : convergence between HPC and data for fluid mechanics

S. Riffaud, "Reduced-order models: convergence between HPC and data for fluid mechanics", PhD thesis, Université de Bordeaux (2020).

Reduced-order model for the BGK equation based on POD and optimal transport

F. Bernard, A. Iollo, S. Riffaud, "Reduced-order model for the BGK equation based on POD and optimal transport", Journal of Computational Physics 373 (2018), p. 545-570.