Department of Mathematics and Statistics



Xiaoli Gao

Xiaoli Gao Research Interests: High Dimensional Data Analysis, Statistical Genetics

Dr. Gao received her B.S. in Probability and Statistics from Anhui University, China in 2000 and her M.S. and Ph.D. in Statistics from the University of Iowa in 2005 and 2008, respectively. Her primary research areas of interest include high-dimensional data analysis, change point, copy number analysis, and survival analysis. With the development of technology, more and more big data sets arise from health sciences, social sciences, and biological sciences. One important question is to find those important features from tens of thousands potential ones. Dr. Gao has special expertise in handling those large-p-small-n problems. She is also looking forward to having a big-data science research group in Triad area. Before joining UNCG, Dr. Gao held an Assistant Professor position at Oakland University in Michigan. She was also served as an intern biometrician at Merck & Co., Inc. in summer 2007 and a research assistant in Statistical consulting center at the University of Iowa in Summer 2005.

Sat Gupta

Sat Gupta Research Interests:Sample Surveys, Biostatistics, Time Series Forecasting

Dr. Gupta received PhD in mathematics from University of Delhi (1977) and PhD in statistics from Colorado State University (1987). He taught at University of Delhi for 6 years, at University of Southern Maine for 18 years, and has been at UNC Greensboro since 2004. He became a Full professor in 1997. His main research area is sampling designs, particularly designs needed for collecting information on sensitive topics where there is a greater likelihood of respondent evasiveness and untruthfulness. He has collaborated with researchers from many fields including biology, marine biology, education, anthropology, psychology, medicine, nursing, and computer science. Some of these collaborative works have been funded by NSF, NIH and other funding agencies.

Scott Richter

Scott Richter Research Interests: Non-Parametric Methods, Multivariate Analysis

Dr. Richter received a PhD in Statistics from Oklahoma State University (1997). His current research interests are in the area of nonparametric methods, especially those based on resampling. Recent work has focused on permutation multiple comparison procedures. As Director of the Statistical Consulting Center, Dr. Richter has consulted extensively with faculty and graduate student researchers from biological, health and social sciences. These interactions have often led to collaborations resulting co-authored publications and externally funded projects.

Haimeng Zhang

Haimeng Zhang Research Interests: Survival Analysis, Spatial Statistics, Applied Probability

Dr. Zhang received his Master's degree in computer engineering in 1996 and his Ph.D. degree in applied mathematics with concentration in statistics in 1998, both from the University of Southern California at Los Angeles, CA. He was an assistant professor and then an associate professor in Concordia College at Moorhead, MN from 1998 to 2008. He became an associate professor of statistics at Mississippi State University from 2008 to 2013. His research interests are in the fields of spatial statistics, survival analysis, and applied probability. He has been collaborating with researchers from many fields such as geography, biology, computer science, and health science. His current NSF-supported research focuses on the statistical analysis of global-scale processes and phenomena using spatio-temporal data collected from global networks and satellite sensors.

Insuk Shim

Insuk Shim My research interest is on Generalization of Multivariate Markovian Arrival Process.The applications were made with level-independent MAP which has only possible one-step transition rate in most cases. If we can generalize MAP that includes level-dependent transitions of MAP which will have finitely many transition rates from one state to other states, it will be more useful to analyze the real situation. Multivariate MAP process can be introduced as a generalization of the MAP. Multivariate MAP can be generally used to count multiple types of events. For example, the resulting probability distributions from the Multivariate MAP show normal, abnormal behaviors, long-tail breakthrough curves depending on the MAP structures. The research will find a rich structure of the Multivariate MAP and flexibility so the result can apply for modeling groundwater solute transport, queueing networks, and other real world problems.