Senior Fellow In Artificial Intelligence at University of Oxford
London, England, France
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Summary
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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.
6 years of coding experience
Doctor of Philosophy - PhD, Modern Statistics and Statistical Machine Learning, Doctor of Philosophy - PhD, Modern Statistics and Statistical Machine Learning at Imperial College London
Bachelor of Science - BS, Economics, 5.71/6, Bachelor of Science - BS, Economics, 5.71/6 at Université de Genève
Regularised B-splines projected Gaussian Process priors
Contributions:1 commit, 8 PRs, 487 pushes in 1 day
bsplinegaussian-processesbayesian-priorssplines
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