Mélodie Monod

Senior Fellow In Artificial Intelligence at University of Oxford

London, England, France
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Summary

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Rockstar
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Top School
Mélodie Monod is a statistician and machine learning researcher with six years’ experience translating complex data into actionable public health insights, most recently as Principal Biostatistician at Novartis and now a Senior Fellow in AI and visiting researcher at top UK institutions. She earned an MSc with distinction and completed a funded PhD in Modern Statistics and Statistical Machine Learning at Imperial College London, where her Bayesian work estimated age-specific infectious disease transmission dynamics. Mélodie combines rigorous academic research—ongoing as an Honorary Research Associate at Imperial and collaborator with Oxford—with hands-on expertise in R, Stan, and Python to build open, reproducible statistical tools. Her applied portfolio spans mobile-phone, spatio-temporal, time-to-event, survey and genomic sequencing data, driving interventions across public health contexts. Notably, she was the top undergraduate in Economics and Statistics in Geneva and the first student to finish her CDT cohort, reflecting both deep technical skill and consistent academic leadership.
code6 years of coding experience
bookDoctor of Philosophy - PhD, Modern Statistics and Statistical Machine Learning, Doctor of Philosophy - PhD, Modern Statistics and Statistical Machine Learning at Imperial College London
bookBachelor of Science - BS, Economics, 5.71/6, Bachelor of Science - BS, Economics, 5.71/6 at Université de Genève
languagesFrench, English
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Github Skills (32)

bayesian-statistics10
corona10
survival-analysis10
sports-data10
statistical-inference10
statistical-models10
probabilistic-models10
next-generation-sequencing9
pytorch9
python9
genome9
sequencing9
infectious-diseases9
pathogen9
splines9

Programming languages (6)

RTeXStanHTMLJupyter NotebookPython

Github contributions (5)

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Extract COVID-19 age-specific mortality counts in U.S states and metropolitan areas
Contributions:1 release, 1246 commits, 6 PRs in 11 months
agecountsmortalitysports-dataareas
Regularised B-splines projected Gaussian Process priors
Contributions:1 commit, 8 PRs, 487 pushes in 1 day
bsplinegaussian-processesbayesian-priorssplines
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