I have a deep love of learning, and statistics provides the perfect foundation for it. By bringing together mathematics, computing, and real-world applications, statistics helps us characterize and quantify both what we know and what we do not know. This perspective shapes my teaching and research. In the classroom, I strive to help students connect theory, computation, and applications in meaningful ways. My research focuses on Bayesian methods for spatial and spatio-temporal data, motivated by a desire to better understand complex processes and our uncertainty about them. I also value being an active member of the statistics community, where I continually learn from colleagues with diverse experiences and perspectives.

Education

• Ph.D. in Statistics, Department of Statistics, The Ohio State University, 2010.
  Dissertation: Bayesian Probit Regression Models for Spatially-Dependent Categorical Data
  Advisor: Catherine A. Calder
• M.S. in Statistics, Department of Statistics, The Ohio State University, 2007.
• B.S. in Actuarial Science, Brigham Young University, 2005.

Affiliations

• American Statistical Association
• Institute of Mathematical Statistics
• International Society for Bayesian Analysis
• The International Environmetrics Society