REU Site in Computational Statistics

Research Mentors

Dr. Sat Gupta (PI)- sngupta@uncg.edu

Dr. Sat Gupta will serve as PI for this project. Dr Gupta is a Professor and Head of the Department of Mathematics and Statistics at UNC Greensboro. He has earned PhD degrees in both Mathematicsand Statistics. He is a Fellow of the American Statistical Association and has won many awardsincluding the UNC Greensboro’s Senior Research Excellence Award (2017). His main area of researchis Survey Sampling with particular interest in surveys involving sensitive topics. Included among his 135+ journal articles with students at all levels including undergraduate students. Dr Gupta was the Site PI for another ASA REU program in the Summer of 2018.

http://www.uncg.edu/~sngupta/

Dr. Xiaoli Gao (Co-PI)- x_gao2@uncg.edu

Dr. Xiaoli Gao will serve as Co-PI for this project. She received both M.S. and PhD degrees in Statistics from University of Iowa. Dr. Gao’s research explores both theoretical investigation of high-dimensional data analysis (HDDA) and its applications in biological and medical studies. In particular, she is interested in robust and complex HDDA, signal approximation and HD shrinkage analysis. During her academic career, Dr. Gao has received several research grants including principal investigator of a 5-year Simons Foundation Grant and UNCG Strategic Seed Grant {Community-Engaged Research and Creative Activity. She was also a Site Co-PI for UNCG ASA REU grant in 2018.

http://www.uncg.edu/~x_gao2/

John Stufken (Senior Personnel)- j_stufke@uncg.edu  

Dr. John Stufken, a statistician, is Bank of America Excellence Professor and Director for the new MS degree program in Informatics and Analytics. He joined UNCG in 2019. His interests are in design of experiments, subdata selection, and data science. He is an elected Fellow of the American Statistical Association and of the Institute of Mathematical Statistics. He was Rothschild Distinguished Visiting Fellow at the Isaac Newton Institute of Mathematical Sciences in Cambridge, UK, and was the inaugural endowed Charles Wexler Professor of Statistics at Arizona State University. He provided leadership in statistics, at both the undergraduate and graduate levels, at Arizona State University (2014-19) and the University of Georgia (2003-14) as Coordinator of Statistics and Head of the Department of Statistics, respectively. Stufken’s research has been partially supported by the NSF throughout his career.

https://sites.google.com/uncg.edu/johnstufken/home

Somya Mohanty (Senior Personnel)- mohanty.somya@uncg.edu

Dr. Somya Mohanty will serve as a Senior Personnel for this project. He received a Ph.D. degree in Computer Science and Engineering from Mississippi State University. His research interests include big-data, machine learning, data science, cyber-security, and trustworthy computing. This research generated approximately $950,000 in research funding from National Oceanic and Atmospheric Administration (NOAA), Department of Defense (DoD), Mississippi State Department of Health (MSDH), and Mississippi Agriculture and Forestry Experiment Station (MAFES). In 2018, Mohanty was involved as a Site Co-PI on the ASA REU program.

http://www.somyamohanty.com/

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, especiallymethods 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/

Jianping Sun (Senior Personnel)- 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

Research Projects

• Potential Research Projects
• Relevant References

Abstracts:

A cluster-based approach to feature detection in persistence diagrams

Topological data analysis, and in particular persistence diagrams, are gaining popularity as tools for extracting topological information from noisy point cloud and digital data.  Persistence diagrams track topological features in the form of kk-dimensional holes in the data. Here we construct an automated approach for identifying the features most likely due to noise so that they may be removed from estimates of the Betti numbers that give direct counts of holes of each dimension.  This approach extends the established practice of using a lifespan cutoff on the features in order to take advantage of the observation that noisy features typically appear in clusters in the persistence diagram.  We show that in many cases with high levels of noise, our method is an improvement over both a lifespan cutoff and the PD Thresholding technique.  This work is motivated by 3-dimensional micro-CT imaging of ice core samples and is applicable for separating noise from signal in persistence diagrams from noisy data.

Topological estimation of image data via subsampling

We develop a novel statistical approach to estimate topological information from large, noisy images.  Our main motivation is to measure pore microstructure in 3-dimensional X-ray micro-computed tomography (micro-CT) images of ice cores.  The pore space in these samples is where gas can move and get trapped within the ice column and is of interest to climate scientists.  While the field of topological data analysis offers tools (e.g. lifespan cutoff and PD Thresholding) for estimating topological information in noisy images, direct application of these techniques becomes infeasible as image size and noise levels grow.  Our approach uses image subsampling to estimate the number of holes of a prescribed size range in a computationally feasible manner.  In applications where holes naturally have a known size range on a smaller scale than the full image, this approach offers a means of estimating Betti numbers, or global counts of holes of various dimensions, via subsampling of the image.

Confidence Intervals and Improved Tests for Mixed Paired-Unpaired Data

We compare confidence intervals for permutation and asymptotic combined tests, and investigate the effect of ties on combined tests

R Simulations of a Unified Mixed- Effects Model

This research focuses on R simulations based on the article, “A Unified Mixed-Effects Model for Rare-Variant Association in Sequencing Studies.” The Mixed Effect Score Test (MiST) is a test to determine the association between a set of SNPS/genes and continuous or binary outcomes by including variant characteristic information and using (weighted) score statistics. Like other gene or region based tests, MiST evaluates the effects of multiple genetic variants in a gene or region by increasing power when multiple variants in the group are associated with a given disease or trait. This analysis compares many commonly used tests for rare variant associations, the Burden Test and the Sequence Kernel Association Test. We examined whether the MiST is more sensitive to type I error inflation. Our approach provides in-depth insight into the general testing framework of the MiST package. We used simulations under a wide range of scenarios to determine if error distributions will affect type I error. In particular, we consider three different distributions for our simulations; normal distribution, t distribution, and gamma distribution. For each distribution, we compared type I error rates at varying significance levels. Results from the study found that if the error is not normal distributed, then this method will result in an inflated type I error.

A Mixture Binary RRT Model with A Unified Measure of Privacy and Efficiency

In this study, we introduce a mixture binary Randomized Response Technique (RRT) model by combining the elements of the Greenberg Unrelated Question model and the Warner Indirect Question model. RRT models are very useful both at the data acquisition stage and at the data release stage because they provide respondent privacy and data security. We account for untruthful responding in the proposed model. A unified measure of model efficiency and respondent privacy is also presented. Finally, we present a simulation study to validate the theoretical findings.

Penalized Weighted Fuzzy Group Variable Selection with Applications in Multi-Omic Integrative Data Analysis

In this project, a high-dimensional penalized weighted regression model (PWR) is considered for simultaneous outlier detection, robust regression and variable selection. In real applications, the data can be irregular due to the contamination from outliers or leverage points and the existence of heteroskedasticity. This phenomenon becomes more common in high-dimensional settings when a large number of predictors are collected. Because of the co-existence of high-dimensionality and data contamination, simultaneous outlier detection and variable selection become important issues. This method has been successfully applied in genetics such as in copy number variation.

Schedule

• Schedule

Eligibility Requirements

Minimum eligibility requirements include:

• Must be a US Citizen or Permanent Resident (Green card)
• Must have completed at least 30 College credits by the end of Spring 2020 with a minimum GPA of 3.0, preferably both math/stats and overall GPA.
• Should have completed the following courses:
  • A probability and statistics course
  • A Calculus course
  • Some programming experience, such as SAS, R, Python. We will reinforce programming skills through our own workshops

Application

Statistics REU 2020 Application Form

The review of applications will begin on February 28, 2020 and will continue until all of the 8 slots are filled.  Completed applications can be sent to Dr. Sat Gupta at sngupta@uncg.edu.

Funding

Selected participants will get:

1. A stipend of $600 per week for 10 weeks, for a total stipend of $6000.
2. Travel support of $1000 per student. This would include funds for non-UNCG participants’ travel to/from Greensboro, NC, as well as funds to support student travel to a regional or national meeting to present their results during the academic year following their REU participation. These monies will be available on a competitive basis, and those students with the most promising results, and without other funding, will be supported. We will also use travel funds for van rental and associated fuel to support scientific/educational trips to research sites.
3. Each participant will receive an annual allowance of $700 for meals and $1,700 for housing.

Housing

It will be apartment style housing on campus with 2-4 students, divided by gender, sharing an apartment with private rooms. More details will be added later.