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.


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:
Feb 10, 2021 My supervisor (Dr. Dutta) and I are holding a (virtual) seminar on Friday 12th February at Imperial College about our recent preprint.
Dec 9, 2020 Our paper describing the likelihood-free inference package ABCpy has been accepted for publication in the Journal of Statistical Software! :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. arXiv
    ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
    Dutta, Ritabrata, Schoengens, Marcel, Pacchiardi, Lorenzo, Ummadisingu, Avinash, Widmer, Nicole, Onnela, Jukka-Pekka, and Mira, Antonietta
    arXiv preprint arXiv:1711.04694 2020