CSCAR Director, Professor of Statistics and Biostatistics
Expertise: Statistical methods for analyzing complex data, including basic to advanced regression, multivariate methods, large-scale data analysis using HPC, development of statistical software in Python and Go, applications to genomics and human biology.
Degree: Ph.D. in Statistics, UCLA
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.