Rob recently received his Ph.D. in Mathematics from Columbia. His training as a pure mathematician has given him strong quantitative skills and experience in using creative problem solving techniques. He has experience conveying abstract concepts to both experts and non-experts and is passionate about the communication of science. Rob, who previously conducted research for the U.S. Department of Defense as an applied mathematician, is excited to be learning techniques in data science, machine learning, and programming so that he can apply his skills outside of academia. While a Fellow at NYC Data Science Academy, Rob tackled several interesting problems in supervised learning and data visualization including making an app for cultural institutions in New York and scraping and visualizing data on the A Song of Ice and Fire (Game of Thrones) book series. His research was in symplectic geometry, specifically enumerative geometry and the foundations of Gromov-Witten theory. His special interests were Kuranishi atlases and other regularization techniques as well as the computations of symplectic capacities. He has presented his research at several universities and conferences such as Brown, Stanford, Columbia, and the Simons Center for Geometry and Physics. His research has also been submitted for publication. In his spare time he can be found listening to jazz, going rock climbing, or attending an opera.
