Research Associate, University of Cambridge
I am a Research Associate at the Leverhulme Centre for the Future of Intelligence at the University of Cambridge, where I develop a framework for evaluating the cognitive capabilities of Large Language Models, together with Prof José Hernández-Orallo and Dr Lucy Cheke.
I previously worked on detecting lying in large language models with Dr Owain Evans and on technical standards for AI for the EU 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, generative models and probabilistic forecasting (with applications to meteorology). My supervisors were Prof. Ritabrata Dutta (Uni. Warwick) and Prof. Geoff Nicholls (Uni. Oxford).
Before 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 carried out my MSc thesis at LightOn, a machine learning startup in Paris.
|Feb 01, 2024
|I co-authored an op-ed on the OECD.AI policy website about how an exemption for “therapeutic purposes” in the EU AI Act could serve as a loophole.
|Jan 16, 2024
|Our paper How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions has been accepted at ICLR 2024!
|Aug 01, 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
|Jul 15, 2023
|Today, I formally graduated from my PhD at Oxford!
|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!
- ICLR 2024How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions2024
- 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