I am a Ph.D. candidate in the Department of Statistics & Data Science and Machine Learning Department at Carnegie Mellon University, and previously completed an M.S. in machine learning at CMU. The area of research I am most passionate about is the intersection of statistics, machine learning, and cosmology. Much of my Ph.D. work has focused on optimally inverting the silhouettes of the intergalactic medium in the spectra of high redshift quasars to reconstruct a full three-dimensional map of the Universe's large-scale structure and obtain probabilities that each mapped structure really exists. On a more theoretical level, my interests span various aspects of supervised learning, spatio-temporal models, and uncertainty quantification. I am fortunate enough to have three fantastic advisors in Larry Wasserman, Jessi Cisewski-Kehe, and Rupert Croft. Before CMU, I received a B.S. in mathematics from the University of Kansas.In my free time I enjoy athletics, reading, traveling, any food wrapped in a tortilla, and my dogs -- Seth and Max. Occasionally, to keep life interesting, a few of my classmates and I team up to compete in hackathons.
10/2018 - Three of my classmates and I took 2nd place in The Data Open at CMU hosted by Citadel and Correlation One (300+ applications, ~100 selected to compete).
07/2018 - I will be speaking about my work on constructing the largest 3D map of the Universe to date at JSM 2018 in Vancouver. Abstract
05/2018 - I will be interning as a data scientist at Uber HQ in San Francisco during the summer of 2018
09/2017 - Three of my classmates and I took 2nd place in the 2017 NBA Hackathon (900+ applications, ~200 selected to compete). Recap
09/2017 - Three of my classmates and I took 2nd place in The Data Open at CMU hosted by Citadel and Correlation One (550+ applications, ~100 selected to compete).