I am interested in developing computational models and algorithms for big data to predict patients' outcomes, which can help clinicians to tailor treatment plans for individual patients. My research lab has developed statistical methodologies for Bayesian analysis, new bioinformatics tools, and gene expression signatures to predict patient prognosis and response to chemotherapy. We are actively developing new bioinformatics tools and computational algorithms for big data, such as imaging and genomic data. Currently, my research is mainly focused on tissue image analysis. Our team was among the first teams to develop and validate computational models using histopathology images collected from routine clinical procedures to refine lung cancer prognosis (Luo et al, 2017, Luo et al, 2019). We have developed deep learning-based models to detect tumor regions, micro-blood-vessels and predict patient outcomes (Wang et al, 2018; Yi et al, 2018; Huang et al, 2017). Recently, we developed algorithms to detect and classify different types of cells from tissue images (Wang et al, 2019; Wang et al, 2020) and developed a set of spatial models (Li et al, 2019, Li et al, 2020) to investigate cell spatial organizations and their implication in disease. Aside from my research in developing statistical methodology, I am also devoted to applying state-of-the-art statistical methods in biomedical research.