Ann W. Clifton

258 Bogard Hall

600 Dan Reneau Dr.

Ruston, LA 71272

About Me

I was born and raised in eastern North Carolina and am an avid outdoorswoman. When I’m not doing mathematics, I enjoy offshore fishing, fly fishing, hunting, hiking, and backpacking.

Appointments

I am an Assistant Professor in the Mathematics and Statistics Program at Louisiana Tech University and an affiliate of the Integrated STEM Education Research Center (ISERC). Prior to this role, I served as an Instructor at Louisiana Tech from 2020 to 2025 and previously taught at Cedar Crest College, Lafayette College, and the University of South Carolina.

Research

My research interests are in structural graph theory and mathematics education. In graph theory, I study vertex partitioning, coloring, and reconstruction problems, with a particular interest in applications and algorithmic complexity. In mathematics education, my work focuses on alternative grading practices, fostering cross-disciplinary connections in student learning, and building a sense of belonging and community through informal learning environments.

Education

I earned my PhD in Computational Analysis and Modeling from Louisiana Tech University in August 2025 under the direction of Galen Turner. I also hold an MS in Mathematics from the University of South Carolina, an MA in Mathematics from East Carolina University, and a BA in Mathematics and Hispanic Studies from East Carolina University.

Teaching

I have taught courses across the undergraduate mathematics curriculum, from College Algebra to Discrete Mathematics and Probability and Statistics. My teaching is student-centered, with the goal that every student leaves my classroom feeling more confident in their abilities as critical thinkers and problem solvers.

selected publications

But Who Should Have Won? Simulating Outcomes of Judging Protocols and Ranking Systems

Ann W. Clifton, and Allison L. Lewis

2022

Abs

We all know the world loves an underdog. But who should have won? Simulation models can give us a way to reproduce contest scenarios using computer power, allowing us to simulate versions of reality numerous times and observe a wide range of possible outcomes. Using basic probability calculations and a wide range of programming tools, we can mimic real life and answer such questions as “how likely is Team A to win the tournament?”, “how biased would Judge B have to be in favor of an athlete for them to come out on top?”, or “is there a particular voting structure that would allow for the emergence of Candidate C as the winner?” In this chapter, we lay out the fundamental skills for pursuing answers to questions like these via simulation, and leave the interested reader with a set of potential research projects on the theme of evaluating fairness, bias, and other issues relevant to ranking schemes and judging protocols.