I am a disease and wildlife ecologist in the Kramer Lab at the University of South Florida-Department of Integrative Biology (USF-IB). My research interests include implementing mathematical/statistical techniques to ecological data in order to understand species invasions, niche structure/habitat suitability, and epidemiological pathways.
My dissertation, “Species distribution modeling with spatiotemporal data and deep learning”, investigates the use of deep learning models built with time series data to classify species distributions and habitat suitability. Other current research projects involve analyzing environmental impact of introduced game birds, Coronavirus transmission network analysis, and creating biologically informative machine learning algorithms.
Prior to my time at USF, I graduated from the University of Florida (Go Gators!), and completed both my Bachelor’s in mathematics (2013) and Master’s in interdisciplinary ecology (2018) from the School of Natural Resources & Environment (SNRE). My undergraduate studies focused on applied analysis, as well as secondary education studies and curriculum theory. For my Master’s thesis, I studied and analyzed the success and failures of introduced Galliformes, particularly Alectoris chukar (chukar partridge).
Aside from academia, I am interested in biking, hiking, and all things outdoors. I am a lead Bird of Prey handler and community educator at Boyd Hill Nature Preserve in St. Petersburg, FL. I am also actively involved in the Florida craft beer community, and a founding member of Cypress & Grove Brewing Co. in Gainesville. FL .
Ph.D. in Biology - Ecology & Evolution, 2024
University of South Florida, Tampa, FL
M.S. in Interdisciplinary Ecology, 2018
University of Florida, Gainesville, FL
B.A. in Mathematics, 2013
University of Florida, Gainesville, FL
A.A. in Mathematics, 2010
Santa Fe College, Gainesville, FL
Under construction
Analyzing records to determine establishment in new places.
Modeling the daily spread of the coronavirus and estimating latent cases
A method to reduce correlative effects in variable seelection in random forest models
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