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Hello, world!

I am a researcher (Inria Starting Faculty Position) in the MATHERIALS group, a joint research team of Inria and École des Ponts ParisTech. My research focus within MATHERIALS is on the development and analysis of efficient and robust sampling methods in high dimensions, particularly for applications related to statistical inverse problems and computational statistical physics.

Most of the scientific software I wrote for my research is available on this Github page. This includes

  • A Python library for Fourier/Hermite spectral methods;

  • A library of Python scripts implementing interacting particle methods for Bayesian inverse problems;

  • A FreeFem++ code for the Cahn-Hilliard equation;

  • A Julia code for calculating the effective diffusion coefficient associated with standard Langevin dynamics, using a variance reduction approach based on control variates.

My GitHub stats

Urbain Vaes's Projects

bayesicle icon bayesicle

Particle methods for Bayesian Inverse problems

bindata icon bindata

A replication of the omonymous R library

cryptpandas icon cryptpandas

Light tool to read and write encrypted pandas dataframes.

hermipy icon hermipy

Python library for the Hermite spectral method

langevin-self-diffusion icon langevin-self-diffusion

Monte Carlo simulation for calculating the effective diffusion coefficient associated with Langevin dynamics

vim-flagship icon vim-flagship

flagship.vim: Configurable and extensible tab line and status line

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