Developed Teas, a Python package to process data products and analyze spectra from the James Webb Space Telescope. Utilized the package to analyze carbon emission features in star-forming regions to investigate how the physical properties of these environments influence the characteristics of interstellar molecules.

Wrote Python scripts to automate validating object classifications in literature-based astronomical databases using satellite data. Expanded on the open-source project Galaxy Data Mines, a command-line tool to crossmatch and compare object classifications between astronomical databases, by updating match algorithms to include newly added object classifications.

Created a Python pipeline to extract light curves from satellite data and measure stellar rotation periods using periodogram and Gaussian process regression methods. Employed the pipeline on a dataset of 3,726 stars to identify nearby stars with a high probability of hosting habitable-zone exoplanets. A subset of these stars will be used as the target catalog for POET, an upcoming satellite for exoplanet astrophysics!