Jamie completed an integrated masters in Mathematics at the University of Durham, specialising in Statistics in his final year. His dissertation explored Bayesian emulation with Gaussian processes in a low-data regime. In particular, he generalised existing methods of introducing inductive bias with the TENSE framework. During the programme, Jamie is interested in working on robust uncertainty quantification, causal inference and Bayesian methods for deep learning whilst broadening his knowledge of statistics and machine learning. Whilst primarily interested in developing methodology, he is also interested in applications of machine learning in medicine, where having safe and reliable models is crucial. In his spare time, Jamie is a keen surfer and enjoys many outdoor activities.
Jamie Reason
