I am currently a third year graduate student studying Quantitative Methodology at the University of Georgia in the Educational Psychology Department.

My research focuses on mixed-format assessments. Specifically, I am interested in analyzing the text responses to constructed-response items using topic models and machine learning algorithms. My current research project is investigating and developing a new item response theory ability estimation procedure for mixed-format assessments. This study improves the accuracy and reduces the standard error of ability estimates by incorporating process features extracted from constructed-response items into the estimation procedure.

Previously, I was a graduate student at the University of Nebraska Omaha where I received a master’s degree in Mathematics with a concentration in Statistics. As partial fulfillment for my degree, I developed an application which pulls current stock data for any stock listed on the New York Stock Exchange and models the data using an ARMA + GARCH time series model. A grid search method automates and optimizes the parameters of the ARMA + GARCH model.

While pursuing my master’s degree, I worked as statistician for a consulting company, CATCH Intelligence. Here, I used R, Python, and SAP Predictive Analytics to develop and implement predictive models for clients throughout different industries, such as: energy companies, state government departments, and other private companies. I would present methods, implementation, and results through slide deck presentations and documentation.