My research primarily focuses on the following main themes: 1) development of methods for risk prediction and analyzing treatment effect heterogeneity, 2) Bayesian nonparametrics and Bayesian machine learning methods with a particular emphasis on the use of these methods in the context of survival analysis, 3) statistical methods for analyzing heterogeneity in risk-benefit profiles and for supporting individualized treatment decisions, and 4) development of empirical Bayes and shrinkage methods for high-dimensional statistical applications. I am also broadly interested in collaborative work in biomedical research with a focus on the application of statistics in cancer research.
COntact
WebsiteLocation
Ann Arbor
Methodologies
Bayesian Methods / Causal Inference / Data Integration / Data Mining / Machine Learning / Mathematical and Statistical Modeling / Optimization / Statistics
Applications