What would you like us to know about you?
I have an interdisciplinary background. I graduated with a bachelor's and master's degree in electrical engineering at Brigham Young University. I then obtained a master's in math and a PhD in electrical engineering at the University of Michigan, where I focused on both theory and applications in machine learning and information theory. I then was a postdoc at Yale University in the medical school and the applied math program where I developed methods for analyzing biomedical data. In particular, I developed PHATE, a tool for visualizing high-dimensional, complex data. Other problems I have worked on include data imputation of complex nonlinear data, estimating information theoretic measures (e.g. entropy, divergence, and mutual information) from data, and deep learning applications. I have worked or am currently working with sunspot images, neural EEG data, single-cell data, gut microbiome data, ecological networks, and financial data. I'm currently in the department of mathematics and statistics at Utah State University.