Developing deep neural networks for Global Climate Models (GCMs) to predict rainfall with greater precision with Professor Yuan Wang.

Wrote and tuned graph neural networks to help NASA Orbiting Carbon Observatory satellite characterize atmospheric aerosol layers to give accurate CO2 readings for use in studying effect of CO2 and greenhouse gas changes on climate change. Developing graph neural network called "GeoSAGE-Net" to further this study, presented findings at 2023 December American Geophysical Union (AGU) conference.

Wrote Python programs to web-scrape and streamline data from multiple PFAS databases for use by machine learning programs to conduct above analyses. Constructed visual representations of above PFAS research on 35 PFAS contaminants for research paper submitted to and published in Summer 2023 American Chemical Society's Environmental Science and Technology Water journal.