Wan Tang, PhD

Wan Tang's primary research focuses on: statistical methodological development in fields such as longitudinal data analysis, missing data, nonparametric smoothing methods, and causal inferences, and their application in biomedical studies, especially in psychiatry and behavioral research. He teaches introductory biostatistics courses such as intermediate biostatistics, statistical methods for survival data analysis, and clustered and longitudinal data analysis. Together with co-authors he published a graduate textbook Applied Categorical and Count Data Analysis in the Chapman & Hall/CRC Texts in Statistical Science series, based on their categorical data analysis courses at the University of Rochester. Prior to joining the faculty at Tulane, he was a research associate professor at the University of Rochester. He received his master of arts and doctor of philosophy ain mathematics from the University of Rochester and his master of science in mathematics from Peking University.