Trained in applied mathematics, Dr. Dai has more than 15 years’ experience in methodology development in bioinformatics using approaches of machine learning (including deep learning) and statistical modeling. Dr. Dai has substantial expertise in analyzing protein/DNA sequences, genomic, epigenomic, microbiome, and metabolomic data generated from high throughput platforms. She has collaborated on multiple cancer-related projects sponsored by NIH and other non-profit foundations. Her current research focuses on developing tools and methods for omics data integration for detecting biomarkers and uncovering gene regulatory mechanisms in disease and cancer. Dr. Dai has mentored 9 Ph.D. students and published more than 100 peer-reviewed papers.