My lab works on applicable machine learning methods in Atmospheric Science and Biogeoscience including (but not limited to) the fields of Micrometeorology, Climatology, Carbon, Water and Energy Cycles, and Wildfire Science. We capitalize on the ever-increasing richness of data from remote sensing projects and measurement networks such as atmospheric flux data, species distributions, land cover and land use information, and long-term climate data.
Collaborations are always welcome as we appreciate the (always beneficial) exchange of knowledge and methods across scientific fields.