Rebekah Herrman

Rebekah Herrman is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Tennessee Knoxville, part of the Tickle College of Engineering. Her work bridges quantum computing, combinatorics, and optimization theory.

Education:

• PhD in Mathematics, University of Memphis (2020), advised by Béla Bollobás
• MS in Mathematics, University of Memphis (2017)
• BS in Mathematics and Physics, Christian Brothers University (2014)

Her research focuses on quantum optimization algorithms, particularly the Quantum Approximate Optimization Algorithm (QAOA), graph theory, and quantum machine learning for solving combinatorial problems. She explores theoretical aspects of quantum walks on dynamic graphs and domination in regular networks.

The recent publications reflect a strong trend in applying quantum computing techniques to classical combinatorial optimization, with emphasis on circuit efficiency, dynamic graph modeling, and mathematical bounds. Her work integrates deep theoretical analysis with practical implications for quantum algorithm design.

Scientific Awards:

• DARPA FORWARD Riser (Class of 2022)
• APS Career Mentoring Fellow (2022–2023)

Dr. Herrman leads the Quantum Algorithms and Optimization REU program, mentoring undergraduate researchers in quantum computing. Her current projects are supported by the National Science Foundation and a seed grant from the AI Tenn Initiative, which funds interdisciplinary research with biomedical and computer science collaborators on quantum machine learning models for liver tumor detection. She advises postdoctoral researcher Igor Gaidai and fosters collaborative research across departments.

She is actively involved in building a research group focused on quantum algorithms, with ongoing work in optimizing QAOA circuits and extending graph-theoretic models to dynamic and constrained environments.