Summary
Oscar Clivio is a PhD candidate in Machine Learning at the University of Oxford with a decade of experience applying statistical and ML methods to causal inference and biomedical problems. His work spans academia and industry—ranging from developing VAEs for cell-type annotation and AutoZI for zero-inflation in scRNA-seq to interpretable safety models at Uber and causal-discovery work with large language models during a ServiceNow research visit. He has collaborated with top labs (Berkeley, Yosef Lab) producing peer-reviewed and spotlight presentations at venues like MLCB, UAI workshops and ACIC, and his tools have contributed to scVI. Skilled in probabilistic modelling, representation learning and scalable data engineering (PySpark/Hadoop), he brings both theoretical rigor and production-minded implementation. Colleagues note his knack for turning nuanced statistical questions—like linking transcriptional burst kinetics to zero-inflation—into reproducible code and publishable insights.
10 years of coding experience
2 years of employment as a software developer
Master's degree, Applied Mathematics and Computer Science, Master's degree, Applied Mathematics and Computer Science at Ecole Nationale des Ponts et Chaussées
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at University of Oxford
MPSI, MP*, MPSI, MP* at Lycée Henri Poincaré
Master of Science - MS, Machine Learning (M2 MVA), Master of Science - MS, Machine Learning (M2 MVA) at École Normale Supérieure Paris-Saclay
English, French, Swedish, German