Environmental Data Science Reu
Worked in a Research Experience for Undergraduates (REU) funded by the National Science Foundation grant through the Kansas Biological Survey. Learned R programming language and Empirical Dynamical Modeling (EDM) for analyzing dynamics of ecological systems. Created functions to apply the GPEDM (EDM using Gaussian Processes) model to large environmental data sets from NOAA. My code for this project is available on Github. Received an Undergraduate Research Award through the mathematics department to write an academic paper quantifying the effects of climate change on population dynamics of fish species in the North Atlantic shelf using GPEDM, while explaining the mathematical intuition behind the model. Self-taught mathematical frameworks used in this investigation such as Taken's theorem, S-map, and Gaussian Processes in order to explain the methodology behind various EDM models. My final paper is currently under review by the math department.In total, I spent over 250 hours learning R, coding, self-teaching math concepts and ecological models, and writing the final paper during the fall semester in 2023. This experience was challenging but rewarding, and has led me to desire a full time career in Environmental Data Science.
