Guillem Hurault

Guillem Hurault

Senior Data Scientist

Professional Summary

I build probabilistic models of noisy real-world systems to improve decision making. Right now that means forecasting the outcome of sports events; previously it was electricity demand and prices, and before that the progression of eczema severity in patients.

In particular, I developed expertise in Bayesian modelling, time-series forecasting, and decision analysis. I am also interested in the engineering around models, in order to deploy reproducible, maintainable, and well-tested solutions in production.

Education

PhD in Statistical Machine Learning

2022

Imperial College London (UK)

Master in Engineering

2018

Ecole Centrale de Lyon (FR)

MSc in Biomedical Engineering

2017

Imperial College London (UK)

Bachelor in Economics

2016

Université Lyon 2 (FR)

Interests

Statistics & Machine Learning Bayesian modelling Time-series forecasting Decision analysis MLOps Software Engineering
Projects
Python analysis template featured image

Python analysis template

Template repository for data science projects in Python.

PhD project featured image

PhD project

Towards a data-driven personalised management of Atopic Dermatitis severity.

Reproducible R Workflow featured image

Reproducible R Workflow

Example reproducible workflow in R.

EczemaPred featured image

EczemaPred

R package to predict eczema.

Streetmaps featured image

Streetmaps

Making streetmaps using OpenStreetMap and ggplot2 in R.

Football Prediction featured image

Football Prediction

Modelling football outcomes in Stan.

LaTeX Templates featured image

LaTeX Templates

Custom style files for LaTeX documents.

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HuraultMisc

Personal R package.

Calcium imaging featured image

Calcium imaging

Shiny app for calcium imaging curve analysis.

Lokta-Volterra competition model in Stan featured image

Lokta-Volterra competition model in Stan

Fitting a two-species Lokta-Volterra competition model using data from multiple experiments in Stan.

Regularisation featured image

Regularisation

Case study comparing different regularisation methods for statistics and Machine Learning.

Selected Publications