Undergraduate Research Opportunities

Associate Professor

Status: Open

Dr. Le Bao earned his Ph.D. from the Department of Statistics at the University of Washington, Seattle. He is an associate professor and the chair of the undergraduate research program at the department of statistics, the key technical advisor for the UNAIDS Reference Group, and the project leader for the Diagnostics Modeling Consortium. His research focuses on using statistical models to address global health problems such as disease mapping, people at high risk of infectious diseases, and systematic literature review. http://www.personal.psu.edu/lub14/

Assistant Professor of Statistics

Status: Closed 

Dr. Sam Baugh is a new professor at Penn State (starting Fall 2023). His interests include developing statistical methods to understand the impacts of climate change, hierarchical Bayesian methods, and applied research in psychometrics and social networks. The following projects currently do not have academic-year funding available, but course credit could be earned in the spring if that is of interest and summer funding may be possible.

Climate Change at Local Scales: Multiple lines of evidence show convincingly that human-induced greenhouse gas emissions are primarily responsible for global warming, referring specifically to increases in temperature over the entire globe. This does not necessarily mean that changes in the climate at any particular location are caused by greenhouse gas emissions. While attributing climate change at the global scale is incredibly important, the public and policymakers are mostly interested in the degree to which humans are responsible for climate change at particular locations. To address this need, statistical techniques have recently been developed to allow us to perform inference on the effects of climate change at the local scale in a way that incorporates global-scale information.

This project will involve creating an R-shiny app for displaying the results of this statistical method to the public. In particular, we would like for participants using the app to understand the degree to which greenhouse gas emissions are causing changes in the climate at their location. A particular emphasis is on appropriately conveying to the user the uncertainty and confidence in these relationships.

Item Response/Social Networks: There are many settings where an individual’s social connections may influence their response to a survey or exam. For example, people who associate with drug users may report higher instances of drug use themselves, or academics who collaborate with high-achieving academics may be more likely to have high professional satisfaction. We are interested in cases where we have a survey/evaluation administered at regular intervals where participants are asked about both outcome-related questions (such as drug use or professional satisfaction) as well as about their social network. Two related questions are particularly of interest:

How do changes in the social network over time allow us to make inference about changes in the outcome responses? For example, if an academic’s social network grows to have more/less successful collaborators over time, is this associated with more/less professional satisfaction?

People are likely to not remember everyone that they’ve interacted with when answering a survey. This results in measurement error in our observation of the social network. The uncertainty induced by this measurement error is often not taken into account, but its impact may have a significant impact on existing statistical techniques. 

This project will start with doing exploratory data analysis on datasets related to points one and two above. Once the underlying data is understood, the project will incorporate the application of recently developed statistical techniques to the data, with a goal of understanding the effectiveness of the methods. 

Associate Professor

Status: Closed

As part of this position, the student will examine an open question in the statistical modeling of animal tracking data. GPS tracking of individual animals is an important approach for understanding individual animal behavior. Missing data is very common in animal tracking data, and the most common approach is to take a two-stage approach and (1) first impute missing data and (2) then fit a statistical model to the imputed data. A more statistically rigorous approach would be to fit a statistical model that jointly estimates model parameters and the missing data locations. In this project, the student will learn Bayesian statistical methods, learn statistical models for animal movement, and use that knowledge to conduct a study that compares two-stage approaches with joint estimation approaches to modeling movement.

This project will be supervised by Dr. Ephraim Hanks, Associate Professor of Statistics, and Liz Eisenhauer, PhD student in Statistics. Desired qualifications include a course in Probabilty (STAT 414), solid computing experience in R (STAT 440 is desired, but not required), and other courses or experience in scientific data analysis (i.e, regression, data science, machine learning, applied mathematics). Research credit hours can be earned for this work. While funding is not guaranteed, we will work with interested students to apply for competitive funding and awards from the Eberly College of Science and the Statistics Department that may help pay for summer work.

Assistant Research Professor

Status: Closed 

Neil received his PhD in Mathematics Education from Arizona State University in 2019. He received his MS in Education-Teaching Math from Northwest Missouri State University in 2011, and a BS in Mathematics from Doane University in 2009.

Neil's research in Statistics Education focuses on understanding how students think about and learn statistical concepts. He currently focuses the meanings that students have for distribution and closely related ideas (e.g., randomness, probability, and stochastic processes). In addition to this research, Neil explores the impacts that web applets can have on student learning, the impact of teacher actions on STEM diversity, and developing non-test assessment methods for getting at student understandings

Students will work with Neil to evaluate the use of  Shiny apps in teaching statistical concepts in on-online or face-to-face courses. Students would be involved in developing apps and field testing them in courses as well as in data management and data analysis of the results of education experiments at Penn State (Stat or Data Science majors only, with a working knowledge of R, and 3.0+ GPA, multiple positions).

Professor of Statistics

Status: Closed 

David R. Hunter earned his Ph.D. in statistics from the University of Michigan in 1999, following a math degree from Princeton University in 1992 and two years teaching mathematics at a public high school in New Hampshire.  He has been at Penn State University since 1999, where he is professor of statistics and served as head of the Department of Statistics from 2012 to 2018. He is a fellow of the American Statistical Association. He has published widely on statistical models for networks and is a co-creator of the "statnet" suite of packages for network analysis in R. He co-coined the term "MM algorithms" and has written extensively on this and other EM-like algorithms. He has also extended the theory and computational practice of unsupervised clustering using nonparametric finite mixture models.

Assistant Research Professor of Statistics, Director of Online Programs

Status: Open

Kuruppumullage Don received her Ph.D. in Statistics from Penn State University in 2014. She received her Masters in Statistics from Penn State in 2011, and a B.Sc. (First Class honors) in Statistics from University of Colombo in 2005.

Her research interests include methods statistical computing, statistical genetics, and bioinformatics. Recently she has also started working on some collaborative projects on statistical education.

Kuruppumullage Don joined Penn State as faculty in 2018 and has been serving as the Assistant Director of the Departmental Online Programs since then. Since 2019, she also serves as the the program director for the online Graduate Certificate in Applied Statistics and as a Biostatistician with Clinical and Translational Science Institute. Before joining Penn State, she served as an Assistant Professor of Statistics at University of Rhode Island (2016-2018) and as a Postdoctoral Research Fellow at Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health (2014-2016). Since 2018 she serves as a reviewer for the American Journal of Distance Education.

Associate Research Professor

Status: Closed 

I received my Ph.D. in statistics from the University of Washington. Prior to joining Penn State, I worked at IHME, a global health institute. My research interests include but are not limited to social networks and population size estimation, with applications in social sciences and global health. As the director of the Statistical Consulting Center at Penn State, I collaborate with a broad range of scientists. Over the years of consulting and collaboration, I have maintained a keen curiosity about science and genuinely appreciate the importance of cross-disciplinary research. My research goal is to produce practically meaningful results that solve real scientific questions.

Associate Research Professor; CAUSE Director

Status: Closed 

Matthew Beckman is an Associate Research Professor of Statistics at Penn State and Executive Director of the Consortium for the Advancement of Undergraduate Statistics Education (CAUSE; www.causeweb.org) a national organization providing resources and professional development opportunities to instructors of college-level statistics.

He earned his PhD in Statistics Education and MS in Statistics from the University of Minnesota, and he earned a BS in Mathematics from Penn State University.

Statistics Education Projects

• Develop surveys of statistics educators nationally and work with web analytics to evaluate the value and usage of materials on CAUSEweb.org including the use of songs and cartoons in teaching.  Student would be involved in developing and posting new materials for instructors to optimize classroom use of a variety of resources (Stat or Data Science majors only)

Professor, Associate Dean for Graduate Education, Eberly College of Science

Status: Open

The greatest beauty and value of statistics stem from its role in collaborative cross-disciplinary research. Dr. Slavkovic’s primary research interest is in the area of data privacy and confidentiality, focusing on statistical disclosure limitation, statistical utility and their interplay with tools from computer sciences such as differential privacy, in the context of small and large scale surveys, health and genomic data, network data, and distributed data. Other related past and current research interests include evaluation methods for human performance in virtual environments, statistical data mining, application of statistics to social sciences, algebraic statistics, causal inference, and data fusion and record linkage. Slavkovic is a professor of Statistics, and Associate Dean for Graduate Education in Eberly College of Science at Penn State. She received her Ph.D. from Carnegie Mellon University in 2004. 

Dr. Slavkovic (http://personal.psu.edu/abs12/) and Dr. Reimherr (http://www.personal.psu.edu/mlr36/) currently run a statistical data privacy group in the department of statistics that includes postdocs, graduate and undergraduate students, discussing and developing tools and methods to support data usability and sharing, and validity of statistical modeling and inference, and thus the reproducibility, under constraints of data privacy.

Assistant Professor

Status: Closed 

Song received her PhD in Statistics from the University of Wisconsin-Madison in 2020. She received her BA in Applied Statistics from Yonsei University in 2012.

Her research interests focus on developing methods to overcome statistical and computational challenges in modern data sets, which are often complex, large-scale, and high-dimensional. She is especially interested in developing statistical techniques to help improve our understanding of biological structures from data.

Song has previously worked in the central bank of Korea as a Statistician. She joined Penn State as an Assistant Professor in 2020.

Associate Teaching Professor

Status: Open

Primary research areas of interest are Sports and Education. Although projects are unfunded at this time, students have successfully researched their own personal topics of interest in these areas resulting in presentation or publication. Academic requirements depend on the robustness of the research question(s). Students who have what they believe is an interesting research idea should email me a brief statement that includes an overview of the research idea, hypotheses, possible data resources, statistics (or related) courses completed, programming languages with which they are familiar, GPA, and semester standing.

Associate Professor

Status: Closed

Dr. Lingzhou Xue received his Ph.D. in Statistics from the University of Minnesota in 2012. His research interests include high-dimensional statistics, statistical learning, optimization, econometrics, and statistical applications in biological science, environmental science, and social science.

For Summer 2022, Dr. Xue's research group has two potential openings for the NSF-sponsored Research Experience for Undergraduates Program. If funded, the Summer 2022 REU program will focus on the computing and statistical foundations of data sciences for undergraduate students who are interested in pursuing graduate studies. We welcome applications from students who major in all areas of study, including computer science, data science, engineering, mathematics, and statistics.

Each participant will receive a stipend of $8,000 to cover all travel and living expenses. NSF requires an REU participant to be either a US citizen or a US permanent resident.