My name is Maya Maciel-Seidman and I am an Earth and Climate Sciences PhD student at Duke University’s Nicholas School of the Environment. As a member of the Ryan Lab, I broadly study the Greenland Ice Sheet using satellite and UAV remote sensing, regional climate models, and machine learning. My research interests lie in applying remote sensing and machine learning to understand and predict changes in the cryosphere. I am currently developing deep learning models that predict Greenland Ice Sheet meltwater runoff from regional climate model output and in-situ observations, to better constrain predictions of global sea level rise.
Before arriving at Duke, I completed my undergraduate education at Vanderbilt University, where I studied Earth and Environmental Sciences and Data Science. At Vanderbilt, I developed new methodologies for quantifying residential carbon emissions as a researcher in the Climate, Health, and Energy Equity Lab. At the U.S. Naval Research Laboratory’s Remote Sensing Division, I produced random forest algorithms to determine the relationships between climate and active layer thickness, developed convolutional neural networks to delineate and map ice wedge polygons from UAV-based lidar-derived DEMs, and conducted fieldwork in Utqiagvik, Alaska.
When I’m not thinking about geospatial data science or the Arctic, I spend my time rock climbing and kayaking, perfecting my challah recipe, trying to beat my NYT Crossword time (my fastest time is 2 min, 45 sec on a Monday), and cheering on the Commodores. Anchor Down!
