Josh has academic degrees in both Statistics and Computer Science, with deep experience in statistical methodology and use of statistical software. His areas of interest include causal inference, propensity score matching, multiple regression diagnosis, structural equation modeling, software implementation and general computing concerns.
Kerby has collaborated with researchers in many areas of natural and social science, with a particular focus on studies involving high dimensional biological data, including genomics, biological imaging, statistical genetics, chemical informatics, health outcomes, and medical claims data. He also has extensive experience in software development, primarily in Python and Go. His research focuses on computational statistics, and methods for modeling complex dependent data including multilevel, longitudinal, and spatial/temporal data.
Chris is a Statistician in the U-M Department of Ophthalmology and Visual Sciences and formerly was faculty at Oberlin College and the State University of New York at Buffalo. He currently is the lead statistician for the Northeast Regional Core of the Women’s Health Initiative and has additional experience as a statistical consultant for Roswell Park Cancer Institute. His areas of expertise include survival analysis, linear and generalized linear modeling, missing data methods, metabolomics, and analysis of administrative claims data.