Laura is a Schmidt AI in Science Fellow in the department of Atmospheric, Oceanic and Planetary Physics and Associate Research Fellow at Reuben College. She uses machine learning and Bayesian statistics to improve small-scale processes in climate models. Her broader interests lie in leveraging machine learning to improve understanding and simulation of the Earth system. She is particularly interested in uncertainty quantification methods for predicting future climate change.
Previously, she was a postdoctoral researcher at Stanford University, where she worked on calibration and uncertainty quantification of atmospheric gravity waves in climate models. Prior to that, she completed her PhD in machine learning and Bayesian statistics to accelerate climate change projections at the University of Reading.
