The Effect of Architecture on Continual Learning
arXiv Preprint (Jan. 2026)
Geometric Function Theory on Quasiconformally Homogeneous Domains
Proquest (Jan. 2026-Dissertation)
Established rigorous links between local distortion control and global stability for nonlinear mappings, using metric geometry and functional analytic tools to characterize when complex systems exhibit predictable behavior. Explores geometric properties of pre-images of quasiregular mappings (ex. Zorich and Power-type Mapping).
Geometric Function Theory on Quasiconformally Homogeneous Domains
Journal of Mathematical Analysis and Applications (Aug. 2025-approval, Feb. 2026-publication)
Established rigorous links between local distortion control and global stability for nonlinear mappings, using metric geometry and functional analytic tools to characterize when complex systems exhibit predictable behavior.
Cops and Robbers on Toroidal Chess Graphs
Geombinatorics (2021)
Illustrates experience with discrete modeling, graph-based reasoning, and collaborative research, highlighting structured problem solving and translating complex systems into analyzable components.
The Effect of Architecture on Continual Learning
Argonne National Laboratory LANS Research Group Seminar (September 2025)
Presented technically rigorous machine learning research to a highly specialized audience of computational mathematicians, demonstrating deep technical expertise and the ability to lead extended, research-level discussions.
The Mathematics of Machine Learning
Pi Mu Epsilon Keynote, North Central College (April 2025)
Explained the foundations of machine learning and neural networks to undergraduate mathematics students, demonstrating strong skills in translating abstract concepts into accessible explanations.
The Effect of Architecture on the Learning Behavior of Deep Neural Networks
Applications & Innovations Seminar: Northern Illinois University (September 2024)
Presented machine learning research to an audience of pure and theoretical mathematics faculty and graduate students, demonstrating the ability to bridge abstract mathematical reasoning with applied neural network modeling and communicate across subfields.
The Effect of Architecture on the Learning Behavior of Deep Neural Networks
Argonne National Laboratory Graduate Student Symposium (August 2024 & July 2025)
Delivered a concise overview of a complex summer research project to graduate students and faculty, highlighting the ability to prioritize key results and communicate efficiently under time constraints.
Cops and Robbers on Toroidal Chess Graphs with Alternating One-Way Streets
Rall Symposium (April 2021)
Presented mathematical research to a broad, non-technical audience, showcasing the ability to communicate complex ideas clearly across disciplines.
Cops and Robbers on Toroidal Chess Graphs
Nebraska Conference for Undergraduate Women in Mathematics (January 2019)
Presented research to an undergraduate mathematics audience, demonstrating clear exposition and engagement with early-career mathematicians.