I am interested in how machine learning and Bayesian statistics can assist our understanding of the climate and weather. While at Stanford, I will explore how these tools can improve gravity wave parameterisations in atmospheric models. I recently completed my PhD at the University of Reading, which focused on emulating climate models to estimate the surface temperature response to changes in anthropogenic forcings, including both long-lived greenhouse gases and short-lived aerosol pollutants. This research took a Bayesian perspective to learn relationships between climate change patterns and forcings. Prior to this, I studied dynamical systems and fluid dynamics in my MRes, after coming from an undergraduate degree in Physics at Imperial College London. Outside of work, my interests include dancing, running and cycling.

Stanford Advisors