Dr. Hailin Sang is an Assistant Professor of Mathematics at the University of Mississippi. He worked as a NISS postdoc from 2010 to 2011, where he developed new sampling methodology and estimation and evaluated the feasibility of using NASS’s sampling list frame to evaluate misclassification errors for the National Agricultural Statistics Service (NASS) at United States Department of Agriculture (USDA).
Sang grew up in China, where he earned his B.S. in Mathematics from Beijing Normal University in 1994. He later moved to the United States, where he earned Master’s degree in mathematics in 2003 from New Mexico State University. In 2008, he concurrently earned from the University of Connecticut his Master’s degree in statistics and his Ph.D. in Mathematics.
As a statistician, Sang focuses primarily on time series, empirical processes, nonparametric statistics, robust statistics, asymptotic statistics, survey sampling design and analysis, and data mining. His work in these fields has been published in numerous science journals. The latest of these, “On Kernel Estimators of Density for Reversible Markov Chains,” was recently published in Statistics and Probability Letters. He is currently working with two Ph.D. students on time series, nonparametric statistics and robust statistics.