The SUDS Scholar will utilize multi-modal molecular and imaging data to build machine learning framework for cancer risk stratification.

Pertrubation of genetic interactions on genomic and transcriptomics level play critical role in promoting tumorigenesis. Therefore, as systematic understandin of these perturbations will likely provide novel insight into cancer biology and open new therapeutic avenues. As part of this project, we will levergae recent advances in graph-based machine learning methods and large-scale genomics & transcriptomics data to systematically characterize genetic perturbations in various cancer types.