About me

I am a Schmidt AI in Science fellow at the University of Oxford, with a broad interest in the use of artificial intelligence (AI)/machine learning (ML) and Bayesian statistics to advance climate modelling. Specifically, I develop ML subgrid-scale parameterisations for small-scale processes such as convection and atmospheric gravity waves. I am particularly interested in quantifying uncertainties associated with ML parameterisations. Previously, I was a postdoc at Stanford University where I worked on uncertainty quantification of ML and physics-based gravity wave parameterisations and prior to that, I completed my PhD at the University of Reading in climate model emulation and Bayesian statistics.

Research Interests

• Climate model emulators
• Hybrid “AI + physics” climate models
• Subgrid-scale processes and their interaction with larger scales
• High resolution climate models
• Bayesian statistics
• Climate model calibration
• Uncertainty quantification of climate models

Feel free to reach out to me at laura [dot] mansfield [at] physics [dot] ox [dot] ac [dot] uk