Charles R Doss

Charles R Doss is an Associate Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. His research is centered on nonparametric inference, shape-constrained estimation, and continuous treatment effects, with applications in public health and precision medicine. He has led multiple funded research projects from the National Science Foundation and the Department of Health.

Research Interests: Dr. Doss specializes in nonparametric methods, particularly under shape constraints such as convexity and monotonicity. His work includes developing theory and methodology for optimal treatment regimes, continuous treatment effects, and robust inference. Applications span public health, including wastewater surveillance for pathogens like SARS-CoV-2, and statistical learning in high-dimensional settings.

The recent publications show a strong trend in causal inference, robust statistical methods, and interdisciplinary applications, particularly in public health and machine learning. His work often involves theoretical development with practical implementation, as seen in projects on wastewater surveillance and multivariate regression models.

Scientific Awards:

• No specific awards listed in the provided text.

Advising and Grants: Dr. Doss has been the Principal Investigator on three National Science Foundation grants and a Co-Investigator on a Department of Health project related to wastewater surveillance. His funding history from 2017 to 2026 reflects sustained support for methodological and applied statistical research. While no students are listed, his role as PI suggests active mentorship and research leadership.

Labs and Teams: He collaborates with interdisciplinary teams, including public health officials and researchers in virology and environmental science, particularly through the wastewater surveillance project. His network includes researchers from various institutions and disciplines, indicating a collaborative research environment.