pylint-xxp
Personal Pylint plugin.
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
Featured Projects
Python analysis template
Template repository for data science projects in Python.
PhD project
Towards a data-driven personalised management of Atopic Dermatitis severity.
EczemaPred
R package to predict eczema.
Streetmaps
Making streetmaps using OpenStreetMap and ggplot2 in R.
Football Prediction
Modelling football outcomes in Stan.
Selected Publications
(2024).
Data-driven personalised recommendations for eczema treatment using a Bayesian model of severity dynamics.
medRxiv.
(2024).
Evaluation of measurement errors in the Patient-Oriented Eczema Measure (POEM) outcome.
Clinical and Experimental Allergy, 54(3), 207-215.
(2022).
EczemaPred: A computational framework for personalised prediction of eczema severity dynamics.
Clinical and Translational Allergy, 12(3), e12140.
(2022).
Detecting eczema areas in digital images: an impossible task?.
JID Innovations, 2(5), 100133.
(2022).
Can serum biomarkers predict the outcome of systemic immunosuppressive therapy in adult atopic dermatitis patients?.
Skin Health and Disease, 2(1), e77.