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/

Please note, students are strongly encourage to take STAT 497, Introduction to Statistical Research, before collaborating with me.

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

• Prof Beckman seeks to hire approximately four (4) undergraduate research assistants primarily during Summer 2025 as part of a research project related to teaching, learning, and assessment for Statistics Education funded in part by the U.S. National Science Foundation.  Successful applicants will work on a team with faculty and graduate students from the departments of Statistics and Computer Science & Engineering, will help create a unique new dataset of student responses to statistics questions collected from partner institutions across the U.S. and New Zealand, and will gain state-of-the-art knowledge on development of formative assessment questions and rubrics. Strong candidates should have a demonstrated interest in STEM Education. Hours, wages and anticipated periods of employment will vary. Applications should be submitted prior to March 9 in order to ensure full consideration, but the position will remain open until available positions are filled. 
•  

Teaching Professor

Status: Open

Marjorie received her PhD in Statistics from Kansas State University in 1996, her MA and BS in Mathematics from the University of New Mexico in 1992 and 1990, respectively, and an AS in Mathematics from Amarillo College in 1987.

Bond's research team, Motivational Attitudes toward Statistics and Data Science Education Research (MASDER) are developing a family of instruments to measure student's attitudes toward statistics and data science (SDS), instructor's attitudes toward teaching SDS, and the salient environment characteristics that the students and instructor interact. Through funding from NSF, DUE-2013392 grant, the set of instruments of the Survey of Motivational Attitudes toward Statistics (SOMAS) or Data Science (SOMADS) will be available on a website for researchers or instructors to administer and receive the resulting data.

Survey of Attitudes Towards Statistics (SATS) Project

Drawing upon a rich, multi-year database, this project seeks to better understand how students' attitudes might impact their performance in statistics classes. Potential models for exploring these data include multinomial logistic regression as well as the nesting of terms.

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: Open 

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 Stochastics Education (encompassing Statistics, Data Science, Probability, and some Applied Mathematics) focuses on understanding how students think about and learn statistical concepts as well as the development of tools to support student learning and pedagogy. 

Neil has several areas of active research that undergraduate students could potentially take part in. When filling out the application linked above, please be sure to mention which project(s) you are currently interested in.

Students’ Meanings for Distribution: This research project is an extension of Neil’s dissertation work that looked at the meanings for stochastic process students developed as they engaged with a particular sequence of instructional activities. Currently, Neil aims to continue this research, expanding on the meanings that students develop for distribution, stochastic processes, and several other closely related ideas such as randomness and probability. 

Distribution Concept and Electron Configuration: Closely related to Neil’s work with distributions, is his joint work with Morgan Vincent (Chemistry PhD student). They are working on investigating students’ meanings for electron configuration and their conceptualizations of associated probability distributions for electrons. Undergraduate students with interests and background in Chemistry are encouraged to apply to this project.

Developing Computational Thinking: This is a new project that is an outgrowth of joint work with Alyssa Hu (Statistics PhD student) and Matt Beckman. The idea here would be to continue the prior work with a focus on developing instructional materials to help students in Stat184 and Stat380 develop their computational thinking skills.

Mapping Student Networks: In this joint work with Abby Sine (Statistics PhD student), we are working to collect data from a group of students to then map out their social-academic network as it pertains to a particular course. This research attempts to use a novel approach to gather this data through the creation of an app. Undergraduate students with background in app development in both iOS and Android systems and/or using React Native are encouraged to apply to this project.

Shiny Apps and Their Impacts: Neil oversees the Book of Apps for Statistics Teaching (BOAST) which is a collection of web apps mostly designed by students to help students learn statistics and data science ideas. The Shiny App program is the development and research side of BOAST where undergraduate students can be involved. In this program, students would help to improve existing apps, develop new apps, and engage in field testing of apps. Research opportunities include examining how students use the apps and the impact that the apps have on student learning and/or performance. The research in this project covers both qualitative and quantitative methods. Students interested in this project need to be Statistics or Data Science majors, have a working knowledge of R, and have 3.0+ GPA.

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.

Professor

Status: Open

Qunhua, received her Ph.D. in Statistics from University of Washington in 2008.

Her primary research interests concern developing statistical methods for uncovering complicated patterns in large and complex biological data. Her work is at the interface between statistics and biology. She has been developing latent variable models and machine learning techniques to identify and infer scientifically meaningful structures from high-throughput genomic and proteomic data.

Li joined Penn State as an Assistant Professor in 2011. She is also a primary faculty member of the Bioinformatics and Genomics program.

LLM Project

This project explores the capability of large language models (LLMs) in performing data science tasks. Undergraduate students will have the opportunity to participate in designing and generating benchmark datasets, as well as evaluating LLM performance. Prior experience with data analysis and data visualization is required, and proficiency in R or Python is preferred.

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.

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.