Lorenzo Pacchiardi

Affiliations. Department of Statistics, University of Oxford

Department of Statistics

University of Oxford

24-29 St Giles'

Oxford OX1 3LB

United Kingdom

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.


Dec 4, 2021 Our paper describing the likelihood-free inference package ABCpy has been published in the Journal of Statistical Software! :tada: 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! :tada: 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! :tada: :smile:

selected publications and preprints

  1. arXiv
    Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators
    Pacchiardi, Lorenzo, and Dutta, Ritabrata
    arXiv preprint arXiv:2104.03889 2021
  2. arXiv
    Score Matched Conditional Exponential Families for Likelihood-Free Inference
    Pacchiardi, Lorenzo, and Dutta, Ritabrata
    arXiv preprint arXiv:2012.10903 2020
  3. JSS
    ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
    Dutta, Ritabrata, Schoengens, Marcel, Pacchiardi, Lorenzo, Ummadisingu, Avinash, Widmer, Nicole, Künzli, Pierre, Onnela, Jukka-Pekka, and Mira, Antonietta
    Journal of Statistical Software 2021