About me
I’m a Schmidt AI in Science Fellow working on climate modelling and machine learning. After receiving my PhD from Oxford in climate computing with Tim Palmer I did a PostDoc at the Massachusetts Institute of Technology where I developed SpeedyWeather.jl, a modern intermediate-complexity atmospheric model written in Julia. Before I even started my PhD, I had a very comprehensive education in climate physics from different universities in Germany, France, and Norway. During that time, I crossed the Tropical Atlantic on an oceanographic research vessel and spent a winter in Svalbard studying meteorology in the Arctic with weather stations. The combination of fieldwork with the theoretical and computational work of my research has allowed me to see the bigger picture and to better understand the climate system as a whole.
Research interests
- Climate modelling: Atmosphere and ocean, grid-point and spectral, dynamical core development, stochastic parameterizations, turbulence closures.
- Computing: High-performance, low-precision, parallel, CPU vs GPU, number formats, posit arithmetic, stochastic rounding, efficiency.
- Data compression: Lossy and lossless, information theory, data formats.
- Predictability of weather and climate: Chaos, uncertainty, ensemble prediction, error growth, weather forecasting.
- Software engineering: Open source, multiple dispatch and code composability, automatic differentiation, and the Julia programming language.
- Decarbonisation: Aviation's contribution on global warming, carbon footprints, decarbonising science.