I am an Associate Professor in the Department of Statistical Science at University College London, where I co-lead the Fundamentals of Statistical Machine Learning research group. My research focuses on building statistical and machine learning methods which enable the use of large-scale models in the physical, environmental and engineering sciences. I am keen to develop methods to merge large-scale models with data, focusing on both robustness to model misspecification and making use of the structure of these complex models to vastly reduce the scale of the associated computational challenges.

My work has been recognised through a Blackwell-Rosenbluth Award, a Best Paper Award at AISTATS, oral presentations at ICML, AISTATS and UAI, a ‘discussion paper’ in the journal Statistical Science, and an honorary mention for the Savage Award. My students have also received a number of student paper awards from the American Statistical Association and the International Society for Bayesian Analysis. Finally, my work has been funded through several EPSRC grants and an Amazon Research Award.

Prior to joining UCL, I did my undergraduate degree (MMORSE, 2010-2014) at the University of Warwick, then did a PhD (2014-2019) as part of the joint centre for doctoral training between the departments of Statistics at Warwick and Oxford. I then spent a year as a postdoctoral researcher, first in the Department of Mathematics at Imperial College London, then in the Department of Engineering at the University of Cambridge. From 2020-2023, I was also a Group Leader in Data-Centric Engineering at The Alan Turing Institute, the UK’s national institute for Data Science and AI.

For more details on my work, see my Publications page or my Google Scholar profile. Alternatively, you can catch me at one of my Presentations. If you are a student/postdoc interested in working with me, see also this page.