Explores applying machine learning to atomic scale systems, emphasizing symmetry in feature mapping and the construction of rotationally invariant descriptors.
Explores the parameterization of atmospheric processes, including microphysics, turbulence, radiation, convection, and surface processes, to improve forecast accuracy and quantify uncertainties.
Explores the theory and applications of the center of gravity in physics, including the calculation of moments, couple forces, and mechanical advantage in levers.
Explores the application of machine learning in molecular dynamics and materials, emphasizing the creation of meaningful features and the importance of generalizability.
