GIRAFFE - Undergraduate Research Workshop in Graph Theory and Data Science

GIRAFFE Undergraduate Research Workshop

Dates and Workshop Description

June 22–26, 2026 at Winston-Salem State University, Winston-Salem, NC. This week-long workshop will engage undergraduate students and their faculty mentors in original research in the areas of combinatorics and data science. Participants will work in small research groups under the guidance of faculty mentors, culminating in written reports and oral presentations.

Overview

This workshop brings together undergraduate scholars and faculty mentors from multiple institutions for an intensive collaborative research experience. WSSU students and faculty will compose the largest contingent of participants, with additional participants selected through an application process hosted on MathPrograms.org.

Logistical Information

The workshop will be held on the campus of Winston-Salem State University from Sunday, June 22 through Saturday, June 26, 2026. Hotel accommodations will be provided for all participants. Faculty will have individual rooms, and students will be paired with a roommate prior to arrival.

Application Information

Applications are due by April 29, 2026. Applicants will be notified of acceptance by  May 7, 2026. Apply here .

Travel Information

Participants are responsible for arranging their own travel to and from Winston-Salem, NC. Travel expenses will be reimbursed up to $200 upon submission of receipts. If this presents a financial hardship, please indicate this in your application.

Student Stipends

Each student participant will receive an $800 stipend in addition to travel reimbursement, per diem for meals, and hotel accommodations.

Sample Daily Schedule

The workshop follows the American Institute of Mathematics (AIM) workshop model, with substantial research time interspersed with professional development activities.

About GIRAFFE

Undergraduate research is a high-impact practice with measurable effects on student engagement, persistence, and post-graduate success. GIRAFFE seeks to foster community among undergraduate researchers in the mathematical sciences while strengthening WSSU’s reputation for scholastic excellence.

Suitability for Undergraduate Research

Combinatorics and data science require minimal prerequisite knowledge, allowing students to contribute meaningfully while learning tools alongside their research activities. These areas are highly active and position students well for graduate study.

Problems of Interest to Students

Combinatorics applies to real-world problems such as power distribution, disease transmission, and resource allocation. Data science problems will focus on healthcare in Forsyth County and sports marketing applications.

Intended Outcomes

Research Products

Each research group will produce a written report suitable for submission to an undergraduate research journal and present their work at the conclusion of the workshop.

Professional Development

Participants will gain experience in research presentation, technical writing, and collaborative problem solving while building professional networks.

Long-Term Engagement

This workshop is intended to establish a lasting tradition of undergraduate research collaboration among WSSU and its peer institutions.

Project Descriptions

Applicants will be asked to rank their interest in the following projects. Faculty mentors and students will be paired prior to the workshop.

Graph Theory Projects

Project 1: Vertex Fault Tolerant Zero Forcing

This project investigates vertex fault tolerance in zero forcing sets, with applications to quantum control and network monitoring. Students will explore open problems related to propagation time, throttling, and algorithmic bounds.

Project 2: Throttling in Transmission Zero Forcing

Transmission Zero Forcing is a new variant of standard zero forcing that investigates what happens when power decreases as forcing propagates but increases when multiple vertices can force a single vertex.  A key difference between standard and transmission zero forcing is that, in the former, a vertex in a graph can be influenced by at most one other vertex while, in the latter, a vertex can be influenced by all of its neighboring vertices.  As a consequence, to analyze the transmission zero forcing process, we must understand both the graph structure and the propagation time, which leads us to the study of throttling in graphs.  Throttling is an analysis of efficiency, in the sense that it is an attempt to minimize a combined quantity that measures both the resources used to perform a task and the time required to accomplish it.  This project will introduce the concept of throttling to transmission zero forcing.

Data Science Projects

Project 3: Medicaid Promoting Interoperability

This research project gives students the chance to work hands-on with real healthcare data while studying how doctors and clinics adopted electronic health record technology through the Medicaid Promoting Interoperability program in Georgia or other U.S. states. Students will build on previous research from nearby states by extending the analysis to a new setting and exploring why some providers successfully completed the program while others dropped out.
Participants will learn how to clean and analyze large, real-world datasets, build machine learning models, and use modern tools like SHAP to understand and explain how those models make decisions. Along the way, students will experience what applied research looks like in practice—working with messy data, asking meaningful questions, and seeing how data-driven evidence is used to guide healthcare policy and funding decisions that affect real communities.

Project 4: Dear Diary, today I learned NLP

In this project, students will explore quality-of-life data from countries around the world, investigating how metrics like freedom of speech, air quality, and healthcare quality relate to national happiness scores. Working with real-world datasets, students will explore two core research questions: first, can we build a model that predicts a country's happiness score from its other quality-of-life indicators? Second, if we group countries by similarity across these metrics, what natural clusters emerge? and which factors most sharply distinguish one group from another?
Along the way, students will develop a well-rounded data science skill set: data cleaning and wrangling to prepare messy, real-world data for analysis; creating compelling visualizations including geographic and spatial maps; building and evaluating predictive models; and applying unsupervised machine learning techniques such as clustering to discover hidden structure in the data. 

About Winston-Salem State University

Winston-Salem State University is a vibrant academic community dedicated to creative thinking, analytical problem-solving, and service.

Founded in 1892, WSSU is a historically Black constituent institution of the University of North Carolina offering a rigorous liberal education.

It all starts here.