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@robinthibaut

Robin Thibaut

robinthibaut
Berkeley

About

I am a PhD Fellow at Ghent University, Belgium. My research focuses on developing a new framework for experimental design in earth sciences under a Bayesian approach. I have experience in marine geophysical surveys, near-surface geophysics and coding. My research interests include Bayesian statistics, geostatistics, image processing, data science, and machine learning. I am interested in applying these methods to a variety of problems in environmental sciences and earth sciences.

Latest papers

A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area
https://doi.org/10.1016/j.jhydrol.2021.126903

  • Official repository: skbel

A new workflow to incorporate prior information in minimum gradient support (MGS) inversion of electrical resistivity and induced polarization data.
https://doi.org/10.1016/j.jappgeo.2021.104286

Research

ResearchGate

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@robinthibaut

Reaching this goal will help me to be more independent and will help me to spend more time on my open source projects and more time on improving my programming skills and my knowledge. I will be able to work on my passion, programming, without any financial stress.

Featured work

  1. scikit-learn/scikit-learn

    scikit-learn: machine learning in Python

    Python 58,378
  2. scikit-fmm/scikit-fmm

    scikit-fmm is a Python extension module which implements the fast marching method.

    Python 262
  3. robinthibaut/pysgems

    Use SGeMS (Stanford Geostatistical Modeling Software) within Python.

    Python 41
  4. robinthibaut/skbel

    SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.

    Python 20

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