National Research Experiences for Undergraduates Program


James Madison University (JMU) has been chosen for a National Science Foundation Research Experiences for Undergraduates (REU) site in mathematics. All interested undergraduates (who are US citizens or permanent residents) are encouraged to apply for this eight week program, in which students will work in groups of 2-4 under the supervision of their faculty mentors. 

Housing in Harrisonburg, VA during the eight weeks of the program and a $4,750 stipend are supplied, as well as full use of university facilities and travel expenses

Students from groups underrepresented within their disciplines (e.g., women, underrepresented minorities, students with disabilities), veterans of the U.S. Armed Forces, first-generation college students and students from socioeconomically depressed regions (e.g., Appalachia) are especially encouraged to apply.

Here is a link to a flyer.


May 23 2022 - July 15 2022.


Please visit to apply. Application deadline is February 18, 2022.


Public Transportation: A Real World AI, Data Science, And Optimization Project: 

James Madison University (JMU) is located in Harrisonburg City, at the heart of the beautiful and sunny Shenandoah Valley. The Harrisonburg Department of Public Transportation (HDPT) aims to transform its bus routing systems, increasing ridership and optimizing operations, given the constraints on various resources, the fact that the data changes drastically when JMU is in session versus when not in session, and from one semester to the other as students' schedules change. The onset of Covid19 and the resulting transition to online classes had another major effect, making predictions less reliable. This is a data driven mathematical modeling and optimization project, where we would use HDPT data to learn optimal bus routes, optimal resource allocation, and to search for improvement avenues. Come to Harrisonburg, and help us help our city.

Mentors: Hala Nelson and John Webb.

Improving Unsupervised Machine Learning Algorithms for Directed Network Data:

This project focuses on developing machine learning methods in the presence of network data, a collection/system of inter-connected entities, a graph representing such a system. In many sciences—for example sociology, biology, and computer science—units under study often belong to communities, and units within the same community behave similarly. How to identify communities in these sciences is a critical problem given a mathematical framework for network data.

A way to identify communities may be through how units interact with each other; units within a community may be more likely to interact with each other than units across different communities. This is equivalent to viewing units under study as a network, where nodes are units and edges are drawn between two units if they interact with each other. Since members within the same community are more likely to interact with each other, it may follow that cycles may be more prevalent within communities than across communities. However, topological cycles are formed by the arrangement of the edges and nodes in a way that it forms a cycle if the first and last node of it are the same. Thus, the detection of these communities may be aided through the use of cyclic structures.  We will adhere to this philosophy by adopting renewal non-backtracking random walk (RNBRW), which is a restricted variant of a random walk by using RNBRW to quantify the cyclic structure for directed networks thereby improving the performance of existing community detection algorithms in these networks.

The students will gain knowledge on python programming language, popular unsupervised machine learning algorithms, and network data analysis.

Mentors: Behnaz Moradi-Jamei and Dinesh Sharma.

Common reply date

This REU participates in the Mathematical Sciences REU Common Reply Date agreement. Any student who receives an offer from an REU that participates in the Common Reply Date agreement, including this REU, is not required to accept or decline the offer until March 8, 2022 (or later). The goal of the Common Reply Date agreement is to help students make informed decisions, particularly when faced with the potential of receiving offers from multiple REUs. 


Please write to with any questions.

This material is based upon work supported by the National Science Foundation under Grant Number 1950370. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Inclusivity Commitment

The Department of Mathematics and Statistics is committed to creating learning environments that support and are improved by a diversity of thought, perspective, and experience. We affirm that the lives and experiences of Black, Indigenous, and People of Color matter. We recognize that within the study and culture of mathematics and statistics there are deep-rooted and systemic inequalities, racism, and sexism that have disproportionately affected some members of our community. We strive to recognize and reverse these inequities. We embrace all backgrounds, identities, names, and pronouns. We see you, we hear you, and we stand with you. You are welcome in our department.

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