Groundwater is a critical resource for drinking water and food supply, yet great uncertainty in the key drivers of groundwater flux at local and global scales challenges our ability to effectively model future resource availability. Ryan Smith’s research focus is to integrate large-scale remote sensing and geophysical datasets to improve understanding of groundwater systems using process-based and machine learning models. The main datasets he uses are Interferometric Synthetic Aperture Radar (InSAR) and Time-Domain Electromagnetics (TEM), but he also works with gravity and passive remote sensing datasets in the optical and thermal wavelengths. Recent research projects include integrating remote sensing datasets to estimate groundwater use at regional scales; using subsidence data from InSAR as a constraint in groundwater models to improve understanding of groundwater storage change; and investigating the role of anthropogenic activity on arsenic concentrations. Prior to joining CSU, Smith was an Assistant Professor in Geological Engineering at Missouri University of Science and Technology.