Jialiang Hua received his MD in China and his MS in Biostatistics from Columbia University’s Mailman School of Public Health. He has a long-standing interest in bridging clinical practice with statistics to produce meaningful public health outcomes. His research interests encompass machine learning, longitudinal data analysis, and analysis of survey data and data with missing values.

Jialiang has been providing statistical collaboration on NIH-funded research studies since graduation. He has developed complex algorithms for analyzing Medicaid MAX and TAF data and has led statistical analyses to evaluate neuroimaging biomarkers for assessing conversion risks and monitoring treatment responses in early stages of schizophrenia. Using advanced statistical methods, including generalized nonlinear mixed-effects models and mediation models, he has analyzed longitudinal data to assess the effect of gestational SSRI exposure on the development of functional gastrointestinal disorders. Additionally, he has developed predictive models with advanced machine learning techniques to identify risk factors for mania and eating disorder.