Dr. Hailin Sang is an Associate 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, theory on machine learning and deep learning, and survey sampling design and analysis. His work in these fields has been published in numerous science journals. The latest of these, “Least absolute deviation estimation for AR(1) processes with roots close to unity” was recently published in Annals of the Institute of Statistical Mathematics. Sang’s research is supported by the Simons Foundation Grant. Currently he also serves as Associate Editor for the journal Statistics & Probability Letters. He has supervised five Ph.D. students who successfully obtained tenure track positions or postdoc position at research institutes. He is currently working with another Ph.D. student on deep learning.