Dr. Sun has developed considerable expertise in algorithm design and statistical analysis of (epi)genetic sequencing data, as well as in the computational modeling of cancer, such as gene regulatory circuits and cellular automata models. For example, he pioneered research linking intra-tumor heterogeneity with underlying tumor growth dynamics using a data-driven modeling approach of multi-region sequencing (MRS) of solid tumors. He also first introduced regional-assembly into fusion transcript prediction and identified CD74-NRG1 as a potential target of the deadly invasive mucinous subtype of lung cancer. The unique experiences and quantitative training have equipped Sun to initiate a team effort to computationally decompose and model tumor heterogeneity, connecting the multiple facets of tumor evolutionary patterns to clinical features. His group will innovate algorithms and computational methods that advance a mechanistic understanding of tumor evolution and that are broadly utilized by the cancer bioinformatics community.
