Dr. Ryan Smith is an Assistant Professor in the Department of Civil and Environmental Engineering at Colorado State University. His research focus is to develop models (process-based and machine learning) to integrate large-scale remote sensing and geophysical datasets with traditional, in-situ groundwater data. The main datasets he uses are Interferometric Synthetic Aperture Radar (InSAR), which provides estimates of land subsidence with ~5 mm accuracy, and Time-Domain Electromagnetics (TEM), which can image subsurface stratigraphy from the surface to ~200 m. In addition to the above datasets, Dr. Smith also works with gravity and passive remote sensing datasets in the optical and thermal wavelengths. Integrating these datasets into process-based models of subsidence and groundwater flow can both improve model quality and challenge traditional model assumptions that may bias model predictions. Assimilating large remote sensing datasets with machine learning models can unlock the ability to make large-scale predictions of groundwater fluxes, including storage loss. Prior to joining CSU, Dr. Smith was an Assistant Professor in Geological Engineering at Missouri University of Science and Technology.