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. I have mostly worked on Bayesian inference for likelihood-free models, exploiting deep learning tools and generalized Bayesian inference. More recently, I have become interested in probabilistic forecasting, specifically for the field of 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.
|Jun 3, 2022||I have two preprints on arXiv exploring an alternative to adversarial training to train generative networks; the first applies it to probabilistic forecasting, while the second is concerned with Likelihood-Free Inference. I feel that is a promising overlooked approach. Happy to hear any feedback!|
|Mar 16, 2022||I have created a website aimed at listing works on using Neural Networks in Bayesian Likelihood-Free Inference I have added the papers I am aware of, but please contribute others you may know (see “About” page for how).|
|Feb 3, 2022||Our paper discussing a new method for learning summary statistics for Approximate Bayesian Computation has been published in Journal of Machine Learning Research!|
|Dec 4, 2021||Our paper describing the likelihood-free inference package ABCpy has been published in the Journal of Statistical Software! Check here for a quick YouTube video introducing the library.|
|Aug 12, 2021||Our optimal lockdown paper has finally been published by PLOS Computational Biology! Check the media coverage here!|
selected publications and preprints
arXivLikelihood-Free Inference with Generative Neural Networks via Scoring Rule MinimizationarXiv preprint arXiv:2205.15784 2022
arXivProbabilistic Forecasting with Generative Networks via Scoring Rule MinimizationarXiv preprint arXiv:2112.08217 2022
JMLRScore Matched Neural Exponential Families for Likelihood-Free InferenceJournal of Machine Learning Research 2022
JSSABCpy: A High-Performance Computing Perspective to Approximate Bayesian ComputationJournal of Statistical Software 2021