I’m a PhD student at Queen Mary University of London under the supervision of Paulo Rauber. My research focuses on developing reinforcement learning algorithms that are both scalable and sample-efficient, with a particular emphasis on Large Language Models, Vision Language Models, Bayesian methods, and model-based approaches. In addition to my academic work, I have also been working as an AI Developer at xDNA, innovating tools using Generative AI that optimize the workflow of professional fact-checkers (Project Aletheia).
Selected Publications
- R. Sasso, M. Conserva, P. Rauber.
“Posterior Sampling for Deep Reinforcement Learning”
International Conference on Machine Learning (ICML), 2023.
- R. Sasso, M. Sabatelli, Marco Wiering.
“Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning”
Transactions on Machine Learning Research (TMLR), 2023.