REU Site in Computational Statistics

Research Mentors

Dr. Sat GuptaSat Gupta (PI)

Dr. Sat Gupta will serve as PI for this project. Dr Gupta is a Professor in the Department of Mathematics and Statistics at UNC Greensboro. He has earned PhD degrees in both Mathematics and Statistics. He is a Fellow of the American Statistical Association and has won many awards including the UNC Greensboro’s Senior Research Excellence Award (2017). His main area of research is Survey Sampling with particular interest in surveys involving sensitive topics. Included among his 150+ journal articles are journal articles with students at all levels including undergraduate students. Dr Gupta was the Site PI for the previous REU grants also during Summers of 2018, and 2020-22.

Jianping Sun

Jianping Sun (Co-PI)

Dr. Jianping Sun is a statistician who has been working at UNCG since August 2018. She had postdoctoral and industry experiences before joining UNCG. She has interests in both statistical methodology and applied research in analyzing high-dimensional complex genomic data.


Sadia KhalilSadia Khalil (Senior Personnel)

Dr. Sadia Khalil joined the Mathematics and Statistics Department at UNCG in 2022. She holds a PhD in Statistics from the National College of Business Administration and Economics (NCBA&E) Lahore, Pakistan (2017).


Scott RichterScott Richter (Senior Personnel)

Dr. Scott Richter has a PhD in Statistics (Oklahoma State University). He is Director of theStatistical Consulting Center at UNCG. His research involves nonparametric methods, especially methods using resampling. Dr. Richter has received extramural support multiple times as Senior Personnel to train young researchers, including REU and UMB programs sponsored by NSF.

Thomas WeighillThomas Weighill (Senior Personnel)

Dr. Thomas Weighill joined the Mathematics and Statistics Department at UNCG in 2021. Before coming to UNCG, he completed a postdoc at the MGGG Redistricting Lab at Tufts University under the supervision of Moon Duchin. Dr. Weighill’s research uses topological and geometric methods in data science, with a particular focus on geographic and election data.