Lorenzo Pacchiardi

I will soon join the Leverhulme Centre for the Future of Intelligence at the University of Cambridge to develop a robust framework for evaluating the capabilities of AI systems, together with Prof José Hernández-Orallo and Dr Lucy Cheke.

I previously worked on evaluating large language models with Dr Owain Evans and on technical standards for AI for the European AI Act at the Future of Life Institute. I am deeply interested in AI policy (particularly at the EU level).

I obtained a PhD in Statistics and Machine Learning at Oxford, during which I worked on Bayesian simulation-based inference and generative neural networks, building on concepts from meteorology and probabilistic forecasting. My supervisors were Prof. Ritabrata Dutta (Uni. Warwick) and Prof. Geoff Nicholls (Uni. Oxford).

Prior to my PhD studies, I obtained a Bachelor’s degree in Physical Engineering from Politecnico di Torino (Italy) and an MSc in Physics of Complex Systems from Politecnico di Torino and Université Paris-Sud, France. I have carried out my MSc thesis at LightOn, a machine learning startup in Paris.

news

Aug 1, 2023 I am happy to communicate that I will join the Leverhulme Centre for the Future of Intelligence at the University of Cambridge in October 2023 :smile:
Jul 15, 2023 Today, I formally graduated from my PhD at Oxford! :smile:
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!
Nov 17, 2022 On the 9th of November, I successfully defended my PhD thesis :champagne:. 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! :smile:

selected publications and preprints

  1. arXiv
    Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization
    Pacchiardi, Lorenzo, and Dutta, Ritabrata
    arXiv preprint arXiv:2205.15784 2022
  2. arXiv
    Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
    Pacchiardi, Lorenzo, Adewoyin, Rilwan, Dueben, Peter, and Dutta, Ritabrata
    arXiv preprint arXiv:2112.08217 2022
  3. JMLR
    Score Matched Neural Exponential Families for Likelihood-Free Inference
    Pacchiardi, Lorenzo, and Dutta, Ritabrata
    Journal of Machine Learning Research 2022
  4. JSS
    ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
    Dutta, Ritabrata, Schoengens, Marcel, Pacchiardi, Lorenzo, Ummadisingu, Avinash, Widmer, Nicole, KĂĽnzli, Pierre, Onnela, Jukka-Pekka, and Mira, Antonietta
    Journal of Statistical Software 2021