Academic Biography
I am a Doctoral Scholar funded by the NERC UKRI Environmental Research Scholarship. I have a First Class BEng Engineering Degree, the Institute of Civil Engineers Award (2020) and a Master's of Science degree from the University of Oxford with distinction in my thesis and science examinations (2021). I am currently entering the third year of a four year DPhil/PhD course in Physics, and am co-supervised across the Department of Physics (Professor Hannah Christensen) the School of Geography and the Environment (Professor Louise Slater), and ETH Zurich (Professor Manuela Brunner).
My research explores machine learning techniques to understand the physical processes and prediction pathways of hydro-climate extremes in the UK and Europe. I have worked on a project testing the predictability of atmospheric circulation patterns for flood events, and am currently working on a methodological comparison of machine learning (ML) models for the analysis of floods. My expertise are in the development of machine learning architecture, primarily supervised models such as random forest models (regression, classification and quantile), to explore these driving mechanisms and prediction pathways of extremes. I am also using Neural Networks, such as Long Short-Term Memory Networks (LSTMs). I am an advanced coder in Python, and experienced using ML packages like scikit-learn, pytorch, tensorflow and neuralHydrology. I am very interested in extreme events, and the intersection of the analysis of extremes and ML.