|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!
|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 03, 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 “About” page for how).
|Feb 03, 2022
| Our paper discussing a new method for learning summary statistics for Approximate Bayesian Computation has been published in Journal of Machine Learning Research!
|Dec 04, 2021
| Our paper describing the likelihood-free inference package ABCpy has been published in the Journal of Statistical Software! 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! Check the media coverage here!
|Jul 06, 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 09, 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!
|Dec 09, 2020
| Our paper describing the likelihood-free inference package ABCpy has been accepted for publication in the Journal of Statistical Software!