I’m a third year PhD student in statistics at Lancaster University. I am currently working on scalable Gaussian process modelling with an application to environmental datasets. I am very fortunate to have three excellent supervisors: Professor David Leslie and Dr Chris Nemeth from Lancaster’s statistics department, and Dr Paul Young from environmental sciences. Within my PhD I have spent 6 months working in the Supply Chain and Optimisation team at Amazon under the supervision of James Hensman working on building end-to-end emulators for doubly intractable problems. I have also spent 3 months with Mind Foundry working with Alessandra Tosi working on multi-objective Bayesian optimisation problems.
Prior to starting my PhD I worked as a Data Scientist at a natural language processing startup Relative Insight. In this role I spent time thinking about abstractive text summarisation of forum and Twitter data. I have also spent time working on graph theory projects with the Institute for Environmental Analytics and time-series modelling with SAP.
For a more formal description of my background, please see my CV.
Bayesian inference: Gaussian processes, variational inference, probabilistic deep learning
Probability: Stein’s method, optimal transport
Environmental modelling: How can we better model atmospheric pollutants in spatiotemporal fashion?
Outside of my PhD I enjoy spending time in the mountains, either on my bike or on foot.
Sometimes Often the weather up North is a little miserable, so when I can’t get out and cycle, I enjoy carpentry. I particularly enjoy making tables and desks from live edge wood.