Biography

I am a Data Scientist at the BAS Artificial Intelligence Lab (AI Lab) and funded by the Alan Turing Institute’s AI for Science programme. In my work, I aim to enable scientific progress through the application of machine learning algorithms to large datasets from climate models, satellites, and in-situ stations. I’m working on combining satellite and surface data to inform weather sensor placement in Antarctica (more information on the project webpage). My other research work involves the development of a deep learning-based sea ice forecasting system, ‘IceNet’ (initial study published in Nature Communications, for which I was awarded the World Meteorological Organization Young Scientist of the Year Award 2022).

Before BAS, I studied Information & Computer Engineering at Cambridge University, graduating with a double-first. My Master’s research at the Computational & Biological Learning Lab involved the development of a machine learning algorithm for automatically dating ice cores based on seasonally-dependent ice core chemicals. My thesis, ‘GPCore: A Gaussian Process Approach for Inferring Ice Core Chronologies’, was awarded a Distinction and is available on my GitHub.

Supervisory positions:

• Co-supervisor of Ellie Krige’s Cambridge University MSci project: ‘Using IceNet to predict and understand the freeze-up and break-up dates of sea ice in Hudson Bay, Canada’ (1st class)

Research interests

• Explainable deep learning for climate prediction
• Arctic sea ice forecasts for conservation and ecosystem management
• Combining gridded climate data with off-grid surface station observations
• Automated ice core dating algorithms

Collaborations

Alan Turing Institute (ATI) | Computational and Biological Learning Lab (CBL), University of Cambridge | UCL Centre for Artificial Intelligence | WWF