My current research involves the construction of deep-learning models to aid in the positioning of telescopes. We employ various machine-learning algorithms to try and optimize the efficiency of telescope pointing systems. Large telescopes often cost thousands of dollars to operate each night, so streamlining the pointing process would provide not only more time for observation, but would be more cost-efficient as well.

This work covers the dynamics of inelastic particle clusters on the real line. Myself and my fellow researchers in the Washington Experimental Mathematics Lab discovered and proved that these dynamics are governed by a few very interesting geometric 'rules', which could be further applied to problems in path-counting and random walks.

This research involved the improvement of existing computational tools to map the genome present in single cells, as well as the programming of new mathematical analyzation methods to track the 3D organization of said genomes.