About me

I am a Machine Learning Engineer at Open Climate Fix. I use AI to forecast renewable energy, helping us to prepare for the transition to green energy. Until recently, I was a Schmidt AI in Science fellow at the University of Oxford, working on AI/ML and Bayesian statistics to advance weather anc climate modelling. I focused on ML subgrid-scale parameterisations, for small-scale processes like convection and atmospheric gravity waves, and on uncertainty quantification. 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.

Interests

• ML/AI for climate solutions
• Climate model emulators
• Hybrid “AI + physics” climate and weather models
• Bayesian statistics and uncertainty quantification