Introduction
BayesRx is a group of biostatisticians and data scientists working at the intersection of statistics, biology and medicine.  Our statistical methodological interests are mainly in high-dimensional data modeling and Bayesian inference. This includes Bayesian bioinformatics, functional data analyses, graphical models, spatial models, Bayesian semi-/nonparametric models and machine learning. These methods are motivated by modern biomedical technologies generating large and complex-structured datasets such as high-throughput genomics, epigenomics, transcriptomics and proteomics as well as high-resolution neuro- and cancer- imaging. The core philosophy of the group is to leverage the underlying scientific hypotheses to be the motivating factors for development of new bio/statistical methodology.

Research Themes

• Multimodal data integration in cancer. 

• Bayesian graphical/network models for high-throughput genomic and proteomic data. 

• Cancer Imaging and Imaging-genomics. 

• Spatial Biology