Donald Turner

Donald Turner, Ph.D., is a statistician with expertise in Bayesian statistics, machine learning, causal inference, and spatial data analysis. Dr. Turner applies these methods to statistical consulting problems involving complex, high-dimensional data, with a research background in genomics and biomedical science.

Dr. Turner's expertise includes Bayesian hierarchical and nonparametric modeling, clustering and variable selection, regression analysis, causal discovery, and uncertainty quantification. He specializes in interpretable modeling: methods flexible enough to capture nonlinear and heterogeneous structure in data while producing results that can be clearly explained, such as explicit equations describing causal relationships.

Dr. Turner has worked with biological and biomedical data, including spatial transcriptomics, gene expression, protein-signaling networks, and cancer research data. He is proficient in R and Python, with additional experience in C/C++, SQL, and high-performance computing.

Dr. Turner received a Ph.D. in Statistics from Texas A&M University, where his dissertation research developed Bayesian methods for clustering spatial transcriptomic data and algorithms for interpretable nonlinear causal discovery. During graduate school, he also performed statistical analysis for a cancer research study and served as a teaching assistant for statistics courses.