The Statistics faculty is comprised of a highly-active research group of statisticians who have a wide range of research interests. Statistics research is currently being conducted in Bayesian statistics, biostatistics and epidemiology, time series and spatial modeling, ecological statistics, animal movement models, disease ecology, sports analytics, statistics education, causal inference, measurement error models, sampling, response surface methodology, design of experiments, and statistical quality control. Statistics faculty have received grant funding through either individual or collaborative grants from national agencies such as NSF, NIH, DARPA, U.S. Geological Survey, U.S. Forest Service, National Parks Service, and U.S. Bureau of Reclamation.
Our dedicated faculty teach a wide variety of courses in small classes where students get individual attention. Research encompasses a broad range of theoretical and practical topics. Because members of the faculty are actively engaged in consulting and collaborative interdisciplinary problems, much of the statistical research is directed toward practical problems.
The Statistics graduate program offers degrees at both the M.S. and Ph.D. levels. Through coursework (in particular, statistical consulting and the MS writing project), research seminars, interdisciplinary research opportunities and faculty who place a high value on mentoring, graduate students have the opportunity to engage in a variety of activities that promote intellectually stimulating and successful graduate education and research.
Congratulations on your recent promotion to Associate Professor in 2020!
Dr. Nicole Carnegie
Dr. Carnegie is a biostatistician focusing on methodological questions encountered in HIV prevention and causal inference. She studies the role of social relationships in shaping the spread, treatment and control of disease. For sexually transmitted infections, such as HIV, understanding the structure and evolution of sexual networks over which pathogens spread is critical to identifying control measures. Her research integrates concepts from epidemic modeling, network modeling and causal inference to develop novel tools for assessing the effectiveness of various intervention strategies.