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robinthibaut

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Computation Geoscientist @ Zanskar Geothermal & Minerals. Machine Learning. Experimental Design. Earth Sciences.

Funding Links: https://github.com/sponsors/robinthibaut

GitHub Sponsors Profile

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

Official repository: MGS-public

Research
ResearchGate
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Featured Works

scikit-learn/scikit-learn

scikit-learn: machine learning in Python

Language: Python - Stars: 62277
scikit-fmm/scikit-fmm

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

Language: Python - Stars: 294
robinthibaut/pysgems

Use SGeMS (Stanford Geostatistical Modeling Software) within Python.

Language: Python - Stars: 57
robinthibaut/skbel

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

Language: Python - Stars: 24