Helen Weierbach (she/her) is an Environmental Data Science Research Assistant in the Energy Geosciences Division. Helen’s background is in applied mathematics including uncertainty quantification, model sensitivity analysis, and Bayesian statistical and machine learning. She is broadly interested in applying her mathematical and computational skills to understand how the earth system responds to changing climate conditions.
At the lab she works on two projects: Charuleka Varadharajan’s Early Career Research project, iNAIADS (iNtegration, Artificial Intelligence Analytical Data Services), and the Watershed SFA. For iNAIADS, she focuses on using Machine Learning to model changes in water quality during hydrological disturbance events. With the Watershed SFA, she works in Nicholas Bouskill’s lab group modelling Nitrogen cycling in the East River watershed using a course-resolution semi-distributed model HAN SoMo (High Altitude Nitrogen Suite of Models). Specifically, she is focusing on modelling how Nitrogen cycling changes in response to future climate perturbations (such as increased temperature, changes in the hydrologic cycle and wildfire).