- Machine Learning Research Scientist
I am an ML Research Scientist at the BAS AI Lab, funded by The Alan Turing Institute. My research focuses on using ML algorithms that leverage large datasets from climate models, satellites, and in-situ stations to develop tools for monitoring and adapting to climate change. In my previous work, I have used deep learning with methods from uncertainty quantification, interpretability, and active learning to drive scientific progress and support decision-making.
I currently specialise in the use of neural processes (NPs) in environmental sciences. NPs are versatile meta-learning models that can be applied to tasks such as downscaling, imputing missing satellite data, forecasting, and sensor placement. To accelerate research in this area, I am leading the development of DeepSensor, an open-source Python package. My latest journal paper applies active learning with NPs using DeepSensor to find optimal sensor locations for monitoring weather in Antarctica.
Another significant area of my research involves the development of IceNet, a deep learning system for sea ice forecasting, for which I was awarded the World Meteorological Organization (WMO)’s Young Scientist of the Year Award. Building upon the initial IceNet study published in Nature Communications, I am now PI on a WWF-funded project that explores the use of IceNet’s forecasts to inform decision-making in wildlife conservation.
Prior to joining BAS, I studied Information & Computer Engineering at Cambridge University. For my Master’s project at the Computational & Biological Learning Lab, I designed a machine learning algorithm for automatically dating ice cores. My thesis, ‘GPCore: A Gaussian Process Approach for Inferring Ice Core Chronologies’, is available on my GitHub.
- Co-supervisor of Jonas Scholz’s Cambridge University MPhil project (2023), ‘Sim2Real with Neural Processes’
- Co-supervisor of Ellie Krige’s Cambridge University MSci project (2021), ‘Using IceNet to predict and understand the freeze-up and break-up dates of sea ice in Hudson Bay, Canada’
- Neural processes in environmental sciences
- Active learning for sensor placement
- Deep learning for sea ice forecasting
- Open-source scientific software development
- Explainability in environmental ML
- AI for smart wildlife conservation
Publications from NERC Open Research Archive
Andersson, Tom R. ORCID record for Tom R. Andersson, Hosking, J. Scott ORCID record for J. Scott Hosking, Pérez-Ortiz, María, Paige, Brooks, Elliott, Andrew, Russell, Chris, Law, Stephen, Jones, Daniel C. ORCID record for Daniel C. Jones, Wilkinson, Jeremy, Phillips, Tony ORCID record for Tony Phillips, Byrne, James ORCID record for James Byrne, Tietsche, Steffen, Sarojini, Beena Balan, Blanchard-Wrigglesworth, Eduardo, Aksenov, Yevgeny ORCID record for Yevgeny Aksenov, Downie, Rod, Shuckburgh, Emily ORCID record for Emily Shuckburgh. (2021) Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications, 12. 12 pp. 10.1038/s41467-021-25257-4
Turner, John ORCID record for John Turner, Guarino, Maria Vittoria ORCID record for Maria Vittoria Guarino, Arnatt, Jack, Jena, Babula, Marshall, Gareth J. ORCID record for Gareth J. Marshall, Phillips, Tony ORCID record for Tony Phillips, Bajish, C.C., Clem, Kyle, Wang, Zhaomin, Andersson, Tom ORCID record for Tom Andersson, Murphy, Eugene J. ORCID record for Eugene J. Murphy, Cavanagh, Rachel ORCID record for Rachel Cavanagh. (2020) Recent decrease of summer sea ice in the Weddell Sea, Antarctica. Geophysical Research Letters, 47. 10.1029/2020GL087127
BLOG: Predicting September 2021 Arctic sea ice using artificial intelligence
Blog 24 September, 2021
Artificial intelligence to help predict Arctic sea ice loss
News 26 August, 2021