Research Interests
I am interested in developing and analyzing statistical algorithms for non-standard data such as: networks, text, and high-dimensional data. My research draws on probability, optimization, and statistics/machine learning and is currently driven by problems from neuroscience and the law.

  • Support Vector Machine behavior in high-dimensional settings (paper)
  • Large scale network and text analysis of the US legal system
  • Dimensionality reduction for network valued data with applications to neuroimaging
  • AJIVE: dimensionality reduction for multiple data blocks (Python, R packages)
Brief Bio
I am a graduate student in Statistics at the University of North Carolina at Chapel Hill where I am co-advised by Shankar Bhamidi and J.S Marron. Before coming to North Carolina I studied math and physics at Cornell University where I graduated in 2014 (Cum Laude). As an undergraduate I worked on the CuSat engineering project team and in a bio-phyiscs lab led by Carl Franck. Over summer 2016 I interned for Gamalon, a Bayesian machine learning startup. In addition to research I consult with Reese News Lab on a variety of data driven journalism projects.

In addition to my research, I believe in making data science and the ability to reason about data more accessible to people without an advanced degree or even a traditional STEM background. This means teaching a practical focused combination of programming (R/Python), statistics, and communication skills. With funding from Data@Carolina (and a lot of help) I developed a new course for the undergraduate statistics major, STOR 390: Introduction to Data Science.