The University of Texas MD Anderson Cancer Center
Department of Biostatistics
My research interests lie in developing novel statistical methodology that enables researchers to extract knowledge and insights from increasingly complex biomedical datasets. I also emphasize applying these methods and disseminating the ideas to the broader biomedical research community. Currently I work heavily with genetics and genomics datasets to better understand the underlying causes of cancers and other diseases. In particular, I am focused on integrating different types of data, e.g. environmental, clinical, or other omics data, to better understand the genetic and genomic etiology of disease.
Specific examples of statistical challenges I am working on include: variable selection tools to fine-map causal variants in genetic association studies, methods for inference on indirect effects of genetic variants that are mediated through gene expression or methylation patterns, and frameworks that are able to scalably integrate dozens of genomic annotations in these types of analyses. Examples of datasets where these ideas are applied include publicly available compendiums such as the ENCODE project and the UK Biobank, as well as various large genotyping efforts in lung cancer and other diseases.
Tutorial students will be given the opportunity to pursue various types of multidisciplinary training related to analysis of biomedical big data. Specifically, students may be expected to obtain biological expertise in the domains motivating their quantitative research, develop new statistical methodology, perform simulation studies to test feasibility of existing methods, or build software packages for use by the broader research community, among other responsibilities.
Education & Training
Ph.D., Harvard University, 2017