# Lorenzo Pacchiardi

Department of Statistics

University of Oxford

24-29 St Giles'

Oxford OX1 3LB

United Kingdom

I work on the safety of large language models with Dr Owain Evans and consult on standards for high-risk AI systems for the European AI Act at the Future of Life Institute. I am broadly interested in technical and policy ways to address the societal impact of AI.

During my PhD in Statistics and Machine Learning at Oxford, I mostly worked on inference for generative models, spanning Bayesian simulation-based inference and training methods for generative neural networks. Some of the approaches I developed build on concepts from probabilistic forecasting. My supervisors were Prof. Ritabrata Dutta (Uni. Warwick) and Prof. Geoff Nicholls (Uni. Oxford). I was also interested in conformal prediction, normalizing flows, gradient-based MCMC methods and weather forecasting.

Prior to my PhD studies, I obtained a Bachelorâ€™s 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 UniversitĂ© Paris-Sud, France. I have carried out my MSc thesis at LightOn, a machine learning startup in Paris.

## news

Dec 20, 2022 | I won a travel award at the International Conference for Statistics and Data Science by the Institute of Mathematical Statistics. I really enjoyed the opportunity to present my work there! |
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Nov 17, 2022 | On the 9th of November, I successfully defended my PhD thesis . A profound thanks to my examiners Chris Holmes and Chris Oates for their insightful comments and suggestions. |

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 â€śAboutâ€ť 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! |