- Conducted comparative effectiveness analyses to evaluate various lung cancer screening strategies by developing a microsimulation model that integrates lung cancer development, progression, screening, detection, treatment and survival. - Worked as part of an NIH-funded consortium, Cancer Intervention and Surveillance Modeling Network (CISNET) that guided the decision-making process of U.S. Preventive Services Task Force for updating their recommendations for lung cancer screening.-… Show more - Conducted comparative effectiveness analyses to evaluate various lung cancer screening strategies by developing a microsimulation model that integrates lung cancer development, progression, screening, detection, treatment and survival. - Worked as part of an NIH-funded consortium, Cancer Intervention and Surveillance Modeling Network (CISNET) that guided the decision-making process of U.S. Preventive Services Task Force for updating their recommendations for lung cancer screening.- Currently developing prediction models for lung cancer incorporating GWAS findings and investigating the effectiveness of risk-based cancer screening strategies.The major goal is to examine whether cancer screening strategies incorporating genetic risk factors contribute to the efficient identification of high-risked individuals to be screened and hence the detection of cancer cases. Show less

Quantitative Sciences Unit, Stanford Center for Biomedical Research (BMIR),Neurosurgery and Medicine

- Jointed the JNCI Editorial Board as a Statistical Editor (http://www.oxfordjournals.org/our_journals/jnci/editorial_board.html)

Biostatistics Branch (BB), Division of Cancer Epidemiology & Genetics (DCEG), National Cancer Institute (NCI), National Institutes of Health (NIH)- Developed several statistical methods for identifying genetic associations and gene-environment interactions using genome-wide association studies (GWAS) data. - Developed a unified framework for testing genetic associations, integrating the effects of environmental exposures to allow for heterogeneous genetic effects by exposure… Show more Biostatistics Branch (BB), Division of Cancer Epidemiology & Genetics (DCEG), National Cancer Institute (NCI), National Institutes of Health (NIH)- Developed several statistical methods for identifying genetic associations and gene-environment interactions using genome-wide association studies (GWAS) data. - Developed a unified framework for testing genetic associations, integrating the effects of environmental exposures to allow for heterogeneous genetic effects by exposure levels.- Conduced collaborative research for various projects including a GWAS fine-mapping study for renal cell carcinoma, a pathway-based association study for colorectal adenoma, a genetic association study for testicular cancer, and studies for evaluating CT screening strategies for lung cancer. Show less