About

Daniel is a PhD Student in Computational Neuroscience and Machine Learning in the Computational and Biological Learning lab at the University of Cambridge.

His PhD work concentrates on various problems in Reinforcement Learning, including emergent representations in Deep RL under partial observability, generative models and inference for non-stationary RL problems, and compositional representations to study generalisation. He also participates in the Simons Collaboration on Ecological Neuroscience, which aims to understand the representation of affordances in the brain.

Before starting his PhD, Daniel worked on cancer genomics, phylogenetic methods, and ML methods for causal discovery.