Yuanjia Wang, PhD, received her PhD from the Department of Statistics at Columbia University. Her research interests center on developing data-driven approaches to explore the relationship between biomarkers, clinical markers and health outcomes to inform discoveries in disease pathogenesis and increase diagnostic capabilities. She works on methods for biomedical data with complex structure and designs arising from observational studies or clinical trials. Dr. Wang has an NIH-funded study to propose methods to estimate penetrance functions from case-control family studies, kin-cohort studies and family history data with applications to Parkinson’s disease and Huntington’s disease. In particular, her methods provide age-specific risk of Parkinson’s disease for LRRK2 mutation carriers and Parkin mutation carriers, which is an important topic in genetic counseling since clinicians and patients use risk estimates to guide their decisions on choices of preventive treatments and planning for the future. Lastly, she works on new statistical learning methods aiming at personalized disease risk prediction.