My research interests lie at the intersection of Statistics and Machine Learning, focusing mainly on Bayesian optimization. I leverage information-theoretic arguments to provide efficient and reliable hyper-parameter tuning for machine learning systems. My favorite application area is natural language processing (NLP) - where we seek to learn from written and spoken text. Current state-of-the-art NLP systems pose particularly interesting tuning problems, as they can take days (if not weeks!) to train and have many configurable hyper-parameters.  I am supervised by David Leslie (Department. of Mathematics and Statistics) and Paul Rayson (School of Computing and Communications).

I completed my undergraduate degree in Mathematics from the University of Cambridge (2016). After enjoying the statistical courses, I took a research placement in computational genetics at the Welcome Sanger Trust. Post-graduation, I participated in the STOR-i internship, which led to completing an MRes and now (since 2017) working towards a PhD.