Darren Homrighausen

Darren Homrighausen is an Instructional Professor in the Department of Statistics at Texas A&M University, affiliated with the College of Arts & Sciences. His research focuses on statistical learning, machine learning, and statistical education, with contributions to inverse problems, econometric methodologies, and computational statistics. He holds a Bachelors in Economics and Mathematics from the University of Colorado, and a Masters and PhD in Statistics from Carnegie Mellon University, advised by Chris Genovese.

He has extensive teaching experience, having instructed courses ranging from introductory statistics (STAT 211) to advanced topics like Statistical Machine Learning (STAT 675), Applied Analytics (STAT 656), and Categorical Data Analysis (STAT 645). His teaching philosophy emphasizes bridging methodological rigor with practical application, reflected in his course designs and materials available on platforms like GitHub.

Research interests include the implications of computational approximations on statistical performance, macroeconomic forecasting philosophies, and the theoretical underpinnings of methods like Lasso and SVMs. He has developed course materials for multiple institutions and actively engages in statistical education initiatives.