REU Site in Computational Statistics
REU 2024: May 28 – July 31, 2024
Complex Data Analysis Using Statistical and Machine Learning Tools
UNC Greensboro, one of the campuses of the University of North Carolina System, will offer a 10-week REU program from May 28–July 31, 2024, for 10 nationally recruited undergraduate students from mathematical sciences. The program will be funded by NSF grant DMS-2244160.
The focus of the program will be on Complex Data Analysis using Statistical and Machine Learning Tools. The ten students will be divided into 4-5 research teams of up to 2-3 students, each headed by a team of faculty Mentors. Students will be able to choose from a wide range of Projects covering topics such as high dimensional data analysis, machine learning, robust data analysis, genetics, topological data analysis, and data confidentiality.
Emphasis during the training will be on both theory and applications. In addition to focused research on these topics, the program will offer participants a broad professional development training through various workshops and invited lectures. The program will also include plenty of social activities such as weekly working lunches and occasional dinners (paid for by the program), and casual conversations on a variety of topics.
Review of applications will begin on February 1, 2024 and will continue until all the 10 slots are filled.
For Information, contact Program Director Dr. Sat Gupta by email at sngupta@uncg.edu, or the Mathematics and Statistics Department at mathstats@uncg.edu.
Research Mentors
Research Mentors
Sat Gupta (PI) sngupta@uncg.edu
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 (Co-PI) j_sun4@uncg.edu
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.
http://mathstats.uncg.edu/people/directory/sun
Sadia Khalil (Senior Personnel) s_khali2@uncg.edu
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).
https://sites.google.com/view/sadia-khalil
Scott Richter (Senior Personnel) sjricht2@uncg.edu
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.
http://www.uncg.edu/~sjricht2/
Thomas Weighill (Senior Personnel) t_weighill@uncg.edu
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.
Research Projects
Research Projects
REU 2024 Projects
Research Team: (pictured left to right) Duy Thuc Nguyen, Harvard University and Jeffrey Choi, NYU with mentor, Dr. Jianping Sun, UNCG Assistant Professor
Project Title:
“Tensor Product Splines for Analyzing the Effects Of Physical Activity
On Interstitial Glucose Level”
“Prediabetes affects more than one third of US people and 70% of individuals with prediabetes eventually develop type 2 diabetes. However, prediabetes is reversible via lifestyle changes, such as dietary change and physical activity. In this project, we will analyze a dataset on wearable devices collected by Bent et al. (2021) by using a novel statistical model to investigate the impact of physical activity on glucose level, and consequently provide insights in developing personalized activity-based interventions for prediabetic patients.”
Research Team: (pictured left to right) William Staab, UNC-Wilmington and Jacob Kathman, Wake Forest University with their mentor, Dr. Scott Richter, UNCG Professor and Director of Statistical Consulting Center
Project Title:
“Improved Confidence Intervals for Mixed-Paired and Two-Sample Designs”
We are working on a project in nonparametric and robust statistical testing with Dr. Richter. Oftentimes, studies include designs that result in a combination of paired and unpaired observations. This can be due to multiple reasons such as missing data or the inclusion of subjects that do not have suitable matches. We are taking data from these studies, modifying preexisting test statistics, and looking at how the confidence intervals that result from these modifications compare to other statistics.
Research Team: (pictured left to right) UNCG students Kendal Walker and Yessica Arzate with their mentor, Dr. Sadia Khalil, UNCG & Lahore College for Women University (not pictured: co-mentor, Dr. Sat Gupta, UNCG Professor)
Project Title:
“Field Work Validation of a Mixture Binary RRT model that Accounts for Trust and Measurement Errors”
There have been several studies where RRT models have been used in real-life settings. Some examples can be found in “Doping and drug use among athletes (Striegel et al., 2006)”, “Ableism (Ostapczuk & Musch, 2011)”, “Antisemitism (Krumpal, 2012)” and “Homosexuality (Chen et al., 2014)”.
In this Applied project, we attempt to validate the Meche et al. (2024) binary mixture RRT model through a field survey at the UNCG campus. The main goals are to explore how easy or difficult it is to implement the Meche et al. (2024) model, how confusing is the model, and what is the prevalence of inappropriate touching among UNCG students
Pictured is UNCG student Caroline Wyrick and her mentor Dr. Sat Gupta, UNCG Professor (not pictured: co-mentor, Dr. Sadia Khalil, UNCG & Lahore College for Women University)
Project Title: “An Optional Mixture Binary RRT Model that Accounts for Trust and Measurement Errors”
In this project, we carry forward the work of Meche et al. (2024) where trust and measurement errors were accounted for in a complex looking mixture RRT model. We explore the potential benefits in using optional RRT as compared to non-optional RRT that was used in Meche et al. (2024)
Pictured is REU participant Alvan Arulandu, Harvard University and his mentor Dr. Sat Gupta, UNCG Professor
Project Title: “Improving Efficiency and Privacy through Joint Scrambling for
Sensitive Variables”
Quantitative RRT models have become the privacy standard for sensitive variable sampling. Beyond the mean estimators of Warner 1965, Greenberg et al. 1969, and variants, Shou et al. 2024 and Ahmad 2002 have posited kernel density techniques for distribution estimation from additive and multiplicative scrambling models respectively. Our work proposes an alternate sampling method, a distribution estimator which asymptotically outperforms the variance of KDE alternatives, and a mean estimator with comparable efficiency and better privacy protection. We validate our theoretical findings with simulations.
Research Team: (pictured left to right) Alvan Arulandu, Harvard University, Daniel Gottschalk, USC and Alexander Richardson, Bowdoin College with their mentor, Dr. Thomas Weighill, UNCG Assistant Professor and Director of Undergraduate Research (not pictured: co-mentor, Thomas Payne, UNCG PhD student)
Project Title: “Through the Grapevine: Vineyard Distance as a Measure of Topological Dissimilarity”
We motivate, formalize, and bound vineyard distance as a theoretical measure of topological dissimilarity, exploring applications to dimensionality reduction, neural training dynamics, and geospatial data analysis.
