Dr. Ashley Ellenson recently graduated from Oregon State University’s Coastal and Ocean Engineering program with a minor in risk and uncertainty quantification. Her expertise lies in understanding nearshore physical processes and developing data driven models that predict or better observe nearshore coastal phenomena. She has published several journal articles and has served as a peer reviewer for articles that combine machine learning and coastal science content.
Ph.D., Civil Engineering, Oregon State University, Corvallis, Oregon, 2021
M.S., Civil Engineering, Oregon State University, Corvallis, Oregon, 2017
B.S., Earth and Environmental Engineering, Columbia University, New York, New York, 2011
Dr. Ashley Ellenson recently graduated from Oregon State University’s Coastal and Ocean Engineering program with a minor in risk and uncertainty quantification. Her expertise lies in understanding nearshore physical processes and developing data driven models that predict or better observe nearshore coastal phenomena. She has published several journal articles and has served as a peer reviewer for articles that combine machine learning and coastal science content.
Nearshore Evolution Research Using Computer Vision Techniques, Australia and North Carolina As a fellow for the U.S. Army Corps of Engineers, conducted research focused primarily on developing computer vision techniques to recognize nearshore geomorphology in time exposure imagery. Developed a convolutional neural network to recognize beach states at two different locations: Narrabeen, Sydney, Australia; and Duck, North Carolina. Also focused on capturing the alongshore variability of nearshore morphology by developing a labeling framework wherein the morphology was labeled as a multidimensional simplex. Using this observational framework, characterized 30 years of beach states at Duck, North Carolina, and correlated evolving nearshore morphology with environmental forcing factors.
Marine Science
Wave Forecasting Corrections Using Machine Learning, Northeast Pacific Ocean Worked as a National Research Trainee for the National Science Foundation’s Risk and Hazard Training program, and developed uncertainty metrics as a data product for wave forecasts using machine learning ensemble techniques. Collaborated with a social scientist to understand end-user (fishermen) perspectives of uncertainty, ensuring that the uncertainty metric aligned with end-user intuition. The uncertainty metric captured the accuracy of 24-hour forecast output of significant wave height and wave period. The ensemble method was a bagged regression tree and predicted wave model error at several buoy locations across the northeast Pacific Ocean.
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