Personal profile

Professional Information

I am currently an assistant professor in the Department of Educational Psychology. I received my Ph.D. in quantitative psychology at UIUC in 2017. I spent the 2017-2018 academic year as a visiting assistant professor of quantitative psychology at the University of California, Merced, before returning to UIUC in Fall 2018. My primary area of research is in educational and psychological measurement. More broadly, I am interested in the improvement of methods used for analyzing multivariate data in the behavioral sciences as a whole. As a methodologist, I work collaboratively with others in education, psychology, and other behavioral sciences around the UIUC community.


Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign, 2017.

M.A. in Psychology, University of Illinois at Urbana-Champaign, 2013.

M.S. in Statistics, University of Illinois at Urbana-Champaign, 2012.

B.S. in Psychology and Mathematics, Central Michigan University, 2009.

Research Interests

My scholarship is focused primarily on issues concerning measurement in psychology and education. The field of measurement involves the creation of mathematical and technical solutions to many highly practical goals. For instance, measurement has a great impact on society through its use in selecting, classifying, and diagnosing people. Furthermore, measurement allows researchers to test theories regarding such societally important traits that would otherwise be unobservable, such as abilities, attitudes, and personality.

As a member of the measurement field, my research seeks to produce and investigate quantitative methodologies useful for the modeling of psychological and educational traits, the construction of tests and measurement tools, and the investigation of behavioral data. This is primarily through the lens of item response theory (IRT). My scholarship can be summarized in terms of three themes: 1) using response times in test construction; 2) investigating models (four-parameter IRT models) used to account for guessing and slipping in responses; and 3) modeling response processes using tree-based IRT models.


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