Department of Statistics
University of Oxford
24-29 St Giles'
Oxford OX1 3LB
I am a PhD student in Statistics at the University of Oxford, working on Bayesian inference for likelihood-free models with the use of machine learning and deep learning tools. I am interested in applying likelihood-free inference methods to the setting of Numerical Weather Prediction. My supervisors are Dr. Ritabrata Dutta (Uni. Warwick) and Prof. Geoff Nicholls (Uni. Oxford).
Prior to my PhD studies, I obtained a Bachelor degree in Physical Engineering from Politecnico di Torino (Italy). Afterwards, I studied towards an MSc in Physics of Complex Systems awarded by Politecnico di Torino and Université Paris-Sud, France. I have carried out my MSc thesis at LightOn, a machine learning startup in Paris.
|Aug 12, 2021||Our optimal lockdown paper has finally been published by PLOS Computational Biology! Check the media coverage here!|
|Jul 6, 2021||If you missed my contributed talk on my work on “Generalized Bayesian likelihood-free inference using scoring rules estimators” at ISBA 2021 World Meeting, you can find the pre-recorded video here on YouTube.|
|Apr 9, 2021||I’ve just released a preprint on Generalized Bayesian Likelihood-Free Inference using Scoring rules with pseudo-marginal MCMC. We believe it is a promising bridge between these two lines of research. Preliminary version, feedback is welcome!|
|Feb 11, 2021||Our methodological paper on designing an optimal lockdown considering an epidemiological model has been accepted for publication by PLOS Computational Biology!|
|Dec 9, 2020||Our paper describing the likelihood-free inference package ABCpy has been accepted for publication in the Journal of Statistical Software!|
selected publications and preprints
arXivGeneralized Bayesian Likelihood-Free Inference Using Scoring Rules EstimatorsarXiv preprint arXiv:2104.03889 2021
arXivScore Matched Conditional Exponential Families for Likelihood-Free InferencearXiv preprint arXiv:2012.10903 2020
arXivABCpy: A High-Performance Computing Perspective to Approximate Bayesian ComputationarXiv preprint arXiv:1711.04694 2020