OATML

Sergio is a DPhil student in the Random Systems CDT at the University of Oxford, co-supervised by Álvaro Cartea, Yarin Gal, and José Miguel Hernández-Lobato (University of Cambridge). His research interests lie broadly in Probabilistic Machine Learning. In particular, he studies the connections between Gaussian Processes and Deep Learning, and explores advances in Generative Modelling (particularly LLMs, diffusion models, and flow matching). His work has two main aims: to develop theoretically grounded and efficient techniques for quantifying model uncertainty, and to design methods that accelerate training and/or inference in state-of-the-art generative models.

Before arriving in Oxford, Sergio completed the MPhil in Machine Learning and Machine Intelligence at the University of Cambridge and earned a BSc in Theoretical Physics from QMUL. He has prior industry experience in both quantitative finance and big tech. As a Research Scientist Intern at Spotify, he worked at the intersection of Gaussian Processes and Deep Learning theory. Later, as a Quant Research Intern at a hedge fund, he developed an LLM-based systematic trading strategy. He is funded by the Man Group through the Oxford-Man Institute Scholarship.