Lung CT imaging, models of hormone data, Bayesian analysis hierarchical Bayesian modeling, statistical computing
My group is developing methods and software packages for analysis of hormone data. These involve non-linear hierarchical Bayesian modeling and figuring out parameterization to represent biology of interest. We are also figuring out how to apply spatial models developed in statistics to lung CT imaging for various lung diseases. I collaborating with junior faculty and leading efforts to building quantitative research resources for the campus (CCTSI, Center for Innovative Design and Analysis).
Applied Data Analysis, Bayesian methods, computational statistics
- Methods of Linear Regression
- BIOS6623: Advanced Data Analysis
- BIOS7714: Computational Statistics
- BIOS6990: Capstone Prep Course
- PhD in Biostatistics, University of Michigan, 2003
- MS in Biostatistics, University of Michigan, 1999
- BS in Statistics and Mathematics, University of Minnesota, 1996
What makes you passionate about or interested in biostatistics?
I love the biomedical problems we encounter. It makes working with numbers have application and impact. I love finding new signatures in data and transforming the information into something of interest to other biostatisticians and biomedical researchers. This is a diverse job where I am never bored and get to work with interesting people. In my Director role, I love to help faculty create excellent jobs with interesting problems to work on.
Personal Interests & Hobbies
Distance running, distance hiking (thru-hiking!), skiing, my kids and family.