Yang Ning

Yang Ning is an associate professor of statistics and data science. The theme of his research is to develop statistical methods and theory to quantify the uncertainty (confidence interval and hypothesis test) in modern data sets, which are characterized by high dimensionality, complexity, and heterogeneity. He enjoys working at the interface of mathematical statistics, machine learning, and stochastic optimization. He is also interested in applied projects in genomics, neuroscience, epidemiology, and clinical trials. 

He has been awarded the NSF CAREER award and the David Byar Young Investigator Award, and has published papers in Annals of Statistics, Journal of American Statistical Association, Biometrika, and Journal of the Royal Statistical Society.

Prior to joining Cornell, he completed a postdoctoral fellowship at Princeton University, where he developed statistical methods and theory for analyzing big and complex data. Ning obtained a bachelor's degree in mathematics from Fudan University, China, and a Ph.D in biostatistics from Johns Hopkins University.