Our research focuses on developing deep learning and AI models and applying multi-omics approaches to translational cancer research at the intersection of genomics and radiation oncology. In these cutting-edge research areas, my lab has been supported by an NCI K22 Research Career Development Award, an NCI ITCR R21 Developmental Research Grant, an NCI R37 Research Project Grant, and an NCI R01 Research Project Grant. Recently we published on integrating imaging and RNA-seq data to improve outcome prediction in cervical cancer, e.g., in Journal of Clinical Investigation and Clinical Cancer Research. We also pioneered in developing generative adversarial network (GAN) models in gene expression data analysis, published in Patterns (Cell Press). Based on our expertise in computer science, cancer genomics, population health sciences, and radiation oncology, we aim to create novel computational approaches using multi-omics data (genomics, proteomics, metabolomics, imaging) and longitudinal, biological, and clinical data to develop novel biomarkers and interpret how molecular alterations in cancers affect patient responses to therapy. To achieve this goal, we will utilize novel translational deep learning models, radio-genomic/multi-omics analyses, and new mechanistic knowledge to facilitate the prevention, diagnosis, and treatment of cancers to improve patient outcomes