About

Daniel is currently an Academic Visitor at the Reinforcement Learning and Artificial Intelligence (RLAI) Lab at the University of Alberta, working on RL theory under the supervision of Prof. Csaba Szepesvári.

He is working towards a PhD in Computational Neuroscience and Machine Learning in the Computational and Biological Learning lab at the University of Cambridge.

His research concentrates on various problems in Reinforcement Learning, including controller-representation co-design, the combination of model-based and model-free approaches, 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 turning to RL, Daniel worked on cancer genomics, phylogenetic methods, and ML methods for causal discovery.