Doctoral Student
CurrentDissertation work focused on studying the properties of cosmic voids and void galaxies in state-of-the-art magnetohydrodynamical simulations and ground-based surveys.-Implemented algorithms to detect hundreds of thousands of voids in large, time domain data sets.-Designed and trained a Wasserstein Generative Adversarial Network to generate density maps of the universe. Used the statistics of cosmic voids in the maps as a metric for gauging network training.-Analyzed the star formation of simulated galaxies, creating and visualizing galaxy density maps by writing parallelized code capable of efficiently assigning millions of particles to a grid.