Research Interests
I am interested in developing and analyzing statistical algorithms for non-standard data such as: networks, text, images, and high-dimensional data. My research draws on stochastic processes, high dimensional statistics, deep learning, and optimization. The scientific questions driving my work come from fields including the law, genetics, natural language processing and medical imaging. Some recent projects include

  • Leveraging JIVE to integrate gene expression data with H&E stained tumor biopsy images. A key challenge in this project is creating techniques to explore visual modes of variation of image data when the images are represented with convolutional neural networks.
  • Analyzing probabilistic models for evolving networks which undergo an abrupt change to their dynamics. This includes developing a non-parametric change point estimator for a general class of network models with a change point.
  • Studying Support Vector Machine behavior, particularly in high-dimensional in settings.
  • Large scale network and text analysis of the US legal system.

Brief Bio
I am a graduate student in Statistics at the University of North Carolina at Chapel Hill where I am advised by Shankar Bhamidi and J.S Marron. Before coming to North Carolina I majored in math and physics at Cornell University. 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.

I am committed to making modern, computational methodologies more accessible to statisticians and scientists, as well as those without a traditional STEM background. With funding from Data@Carolina (and a lot of help) I developed and taught the first data science course offered by UNC's statistics major (STOR 390: Introduction to Data Science.)