About me

I am a postdoctoral researcher at Stanford University, interested in how we can use machine learning to improve climate models. I am currently working on gravity wave parameterizations, which are used to represent the subgrid-scale portion of the gravity wave spectrum in climate models. I work on calibration and uncertainty of parameterizations and on machine learning alternatives.

Research Interests

My main interests include machine learning and Bayesian statistics for aiding climate change projection. I am interested in climate model emulation to understand the impacts of climate change at low computational costs. I am also interested in machine learning approaches to improve climate models by creating “hybrid” climate models which combine traditional dynamical solvers with novel machine learning components for subgrid-scale processes.

Feel free to reach out to me at lauraman@stanford.edu