I’m broadly interested in Bayesian machine learning, believing that uncertainty quantification is essential, especially for optimal decision making. I also want to develop general techniques and algorithms which enable inference to be applied to many real-world scenarios, including including improving ‘robustness’ (e.g. traditional adversarial robustness, as well other techniques such as differential privacy) as well as data efficiency (spanning semi-supervised learning to meta-learning).