Kristen’s academic and professional experience in water resources, bridges fields of aquatic ecology, fluvial geomorphology, hydrogeology and environmental engineering. Her current research involves the application of advanced computational tools to address environmental challenges in water resource management and to support pragmatic solutions within an adaptive management framework. She has used machine-learning algorithms and Bayesian inference to evaluate catchment dynamics and biogeochemical processing in rivers. Kristen has applied smart classifiers and Bayesian statistics, to better understand spatial and temporal variability in sediment and nutrient flux, to inform sustainable design of infrastructure for geomorphic and ecological compatibility, and to direct river corridor conservation and restoration activities for reduced flood losses.