- Contributed to the development of a ground-up custom built software pipeline for processing, quality control, and analysis of data from the Murchison Widefield Array Epoch of Reionization Experiment. - Developed a novel machine learning approach to extract, measure, and classify galaxies with high precision and reliability in radio images. - Integrated multi-frequency information from many disparate data sets to inform galaxy classification. - Applied multivariate statistics, regression, and probability density estimation in several instances to fit theoretical models to data.- Used data visualization frequently as a tool to convey scientific results as well to identify and track down instrumental and processing errors. - Effectively communicated results to both experts and non-experts in both oral and written presentations.

I contributed code to Astropy, an open source Python software package for astronomers. My project involved adding new models and functionality to significantly speed computation time when working with large images. I worked under the advisement of core contributors through GitHub version control.

Collaborated with researchers in Melbourne and Perth to advance projects that bridge science teams in the US and Australia.

I developed software to optimize the calibration and imaging process for the Low Frequency Array (LOFAR) Multifrequency Snapshot Sky Survey. This work improved the signal-to-noise and flux accuracy of detected sources. It was also the first multifrequency analysis of a statistically large sample of LOFAR detections, and the first informed estimate of the final catalog size.

Used radio observations from the Arecibo telescope to study the neutral Hydrogen gas component of galaxies in relation to their environment and star formation rates.

Studied the seasonal changes in composition and distribution of species with Saturn's atmosphere using near-infrared observations.

Analyzed the morphology and spectra of emission line galaxies with QSO-like colors.