I am second year graduate student in Quantitative Psychology at the University of California, Davis. I am currently focused on developing Bayesian methods for estimating Gaussian graphical models, for both low and high-dimensional data. The former are commonly found in clinical applications, such as the conditional (in)dependence structure of post-traumatic stress disorder symptoms. High-dimensional data, on the other hand, are common in the research areas of genetics and functional magnetic resonance imaging. These applications often require estimating thousands of parameters, with relatively few observations. This provides a unique challenge, in that sampling from the posteriors must be done efficiently while not sacrificing accuracy.
I practice open science, utilize pre-print servers, and help administrate a facebook methdods disussion group. Additionally, I am a recipient of both the FORD Foundation fellowship and the National Science Foundation Graduate Research Fellowship.