My research utilizes physical experiments, modeling, field work, and remote sensing to better understand (1) the morphodynamics of fluvial, deltaic, and coastal systems; and (2) interactions between vegetation, hydrology, climate, and surface processes. Understanding these complex interactions is essential for predicting landscape change, especially in a changing climate, and requires investigation across a broad range of temporal and spatial scales using a mixture of observational and modeling techniques. The goal is to understand not only how landscapes evolve but also how the processes and process interactions that shape landscapes change under different conditions. Given the rapid warming in the Arctic, much of my work is focused on high latitude landscapes, but I'm interested in morphodynamics, ecogeomorphology, hydrologic connectivity, and coastal change at all latitudes.

Curriculum Vitae: https://tinyurl.com/2xt2dk8y

My work focuses on using preexisting fiberoptic cables for seismic sensing in urban environments. By leveraging the dense spatial sampling DFOS systems provide, I can assign magnitudes to seismic signals. Additionally, I apply a deep learning model based on a convolutional neural network to identify low SNR signals. My work has broad application to monitoring enhanced geothermal systems where monitoring and characterizing low SNR microseismicity is crucial to operational safety.