Undergraduate Research

What Is Undergraduate Research?

Undergraduate research is a valuable opportunity for students to engage in original investigation, apply classroom knowledge to real-world problems, and contribute meaningfully to ongoing academic work. Research can take many forms—from data analysis and modeling to experimental design, theoretical work, and applied computing.

Why Should You Get Involved?

· Gain hands-on experience with tools, techniques, and problem-solving strategies used in professional settings.

· Work closely with faculty mentors who guide and support your academic growth.

· Strengthen your resume or graduate school application with meaningful, sustained experience.

· Explore new ideas, generate insights, and contribute to innovative projects that matter.

Past Student Projects

Here are just a few examples of the incredible research our students have undertaken:

· Under the direction of Dr. Alan Huebner, Tommy Clark and Megan McGinty completed projects involving the reliability or stability of measurements gathered from sports performance science technology such as force plates and markerless motion capture systems. Both were coauthors on their respective publications.

· Under the direction of Dr. Robert Rosenbaum, Gabrielle Thivierge used and analyzed integro-differential equations to model pattern forming dynamics underlying visual hallucination patterns. The project yielded some interesting animations.

· Under the direction of Dr. Daniele Schiavazzi, John D. Lee published a paper focused on developing a new pipeline for probabilistic real-time treatment planning for multiple stenosis in peripheral pulmonary artery disease. The research combined offline assimilation of boundary conditions, model reduction, and training dataset generation with online estimation of marginal probabilities to create a fast surrogate model that could aid in time-critical medical decisions.

Finding a Research Mentor

There are two main ways to find research opportunities in the department:

Search STRAND (Student Research At ND)

Many faculty-led projects are listed on the STRAND platform, a university-wide hub for undergraduate research opportunities. These are structured openings that faculty have proposed and are actively recruiting students for.

Look for projects tagged with your interests or skill set. Make sure to check for deadlines!

Reach Out Directly

Some professors prefer to work with students who come to them with curiosity, initiative, or a specific idea. These faculty members may not have a formal posting on STRAND but are open to mentoring students in a more flexible or exploratory way.

Faculty Open to Research Mentorship

Below is a list of faculty members in the department who are open to mentoring undergraduate researchers. For each, we’ve indicated how best to connect. Please make sure when reaching out to professors that you use proper titles, such as Professor or Dr.

Professor	Research Areas	Credit or Paid	Semesters	How to Get Involved
Martina Bukac	Projects are usually related to my current research topics.	Paid, Credit	Fall, Spring, Summer	Visit Strand; Contact the professor directly
Bei Hu	Tumor behavior.	Credit	Fall, Spring	Contact the professor directly
Alan Huebner	Sports performance science and educational psychology. All of my projects involve R programming and many use a type of statistical method called "mixed regression models".	Paid, Credit	Fall, Spring, Summer	Visit Strand; Contact the professor directly
Michael Pruitt	Projects involving computing and/or numerical analysis.	Credit	Fall, Spring	Contact the professor directly
Daniele Schiavazzi	Numerical hemodynamics, stochastic modeling, machine and reinforcement learning, LLM agents for health	Credit	Fall, Spring	Visit Strand; Contact the professor directly
Tiffany Tang	Applying and developing new interpretable machine learning methods for scientific/biomedical applications as well as developing open-source software for the broader data science community	Credit	Fall, Spring, Summer	Visit Strand; Contact the professor directly
Adam Volk	Introduction to graph theory, readings on an application of interest to the student, and using graphs to model and analyze data or scenarios.	Credit	Fall, Spring, Summer	Contact the professor directly
Charles Wampler	Application of numerical algebraic geometry to questions in kinematics, robotics, computer vision and the like.	Credit	Spring	Contact the professor directly
Victoria Woodard	Applications of statistical and machine learning models. Projects in economics and finance are preferred	Credit	Fall, Spring	Contact the professor directly
Xiufan Yu	Statistical machine learning; causal inference; high dimensional data analysis	Credit	Fall, Spring, Summer	Contact the professor directly