I was formally trained in Mathematics and theoretical Statistics, but my research is very applied. This is reflected in my teaching style. I strive to inculcate strong theoretical fundamentals, while focusing on the applied aspects of Biostatistics. At UWM, we offer a Masters of Public Health (MPH) and a PhD, both with concentrations in Biostatistics. Please contact me if you are interested in applying to either of these programs.


PH 712 Probability and Statistical Inference

This course covers the basics of probability theory and introductory Statistical Inference. It is taught every fall semester. At least two semester of calculus are required as preparation for PH712. The syllabus can be found here.

PH 718 Data Management and Visualization in R

This is an introductory course to statistical computing in the R environment. The focus is on data management and visualization, but we also cover some of the basics of programming such as Boolean operators and for- and while- loops. It is taught every spring. Although helpful, no prior experience in coding is necessary. The syllabus can be found here.

PH 818 Statistical Computing

This course covers the theory and application of common algorithms used in statistical computing. Topics include root finding, optimization, numerical integration, Monte Carlo, Hidden Markov Models, and bootstrapping. Some specific algorithms discussed include: Newton-Raphson, EM, Principal Components, and Cross Validation. Applications of these algorithms to real research problems will be discussed. The syllabus can be found here.

American Association for Cancer Research (AACR) Integrative Molecular Epidemiology Workshop

Starting in August 2014, I have been on the faculty for the AACR Integrative Molecular Epidemiology workshop. This is an intensive 1-week workshop that is offered every summer. I teach modules on germ-line genetic susceptibility to cancer. Other topics include molecular approaches for characterizing tumors and using functional genomic data (e.g., RNA-Seq and ChIP-Seq data) to find biological mechanisms that drive carcinogenesis.