Geometry & Topology
Geometry & Topology
Embedded Files
Currently I'm working on a problem about a specific configuration in complex projective space. The configuration allows for a smooth surgery of two transversely intersecting surfaces. These are the slides from my most recent talk at the Redbud Topology Conference - slides.
Applied Mathematics
Applied Mathematics
Geometric Deep Learning (GDL) is a current field that I'm learning. The field aims to use deep learning techniques on non-Euclidean data structures. Naturally, I've now also developed interest in Optimization, Probability and Statistics - in particular, how they allow you to measure, analyze and model data. For this study, I've been learning Python - in particular, using the numpy library. Data analysis is another field that I'm exploring.
Rethinking Number Theory
Rethinking Number Theory
June 22 - Jul 3, 2026
As a junior researcher, I'm always looking for problems that are at my grasp. The tough thing about research in a new field is figuring what things to actually look at and what's approachable. I'm so grateful for the NSF for providing this option for the summer. If you're an undergraduate student interested in doing research this summer, send me an email! The application will soon be available here.
Publications
Publications
(Dissertation) Smooth Circle Sum Near Transverse Intersections. July 2025
(In progress) Disk Configurations in Complex Projective Space
Presentations & Notes
Presentations & Notes
Redbud Geometry/Topology Conference (Fall 2025) - "The Circle Sum" (slides)
Introduction to Geometric Deep Learning - (slides coming soon!)
Redbud Geometry/Topology Confernce (Spring 2026) - "Circle Sum Constructions" (slides coming soon!)
Seminars & Conferences
Seminars & Conferences
OSU Topology Seminar (2025)
OU Geometry & Topology Seminar (2025)
Redbud Geometry/Topology Conference (Fall 2025)
OURFA2M2 (Spring 2026)
Head Judge @ Math Counts Oklahoma (2026)
Redbud Geometry/Topology Seminar (Spring 2026)
Rethinking Number Theory (Summer 2026)
Research Areas for Undergraduates
Below are some research areas for undergraduates in mathematics, computer science (computational emphasis) and education. For math & cs majors, here are some foundational courses you should look into : calculus, differential equations, linear algebra, probability & statistics, numerical analysis & methods, graph theory & combinatorics, modern algebra, real & complex analysis - for those with 3D graphics or geometric interests, take differential & riemannian geometry.
Mathematics
Mathematics
Optimization
Statistics
AI & Machine Learning
Data Science & Analytics
Bioinformatics
Cybersecurity & Cryptography
Operations Research
Deep Learning
Computer Science
Computer Science
AI & Machine Learning
Data Science & Analytics
Bioinformatics
Cybersecurity & Cryptography
Deep Learning
Education
Education
AI in STEM Education
Increasing Math Awareness
Students and Careers in STEM
Simplifying STEM Instruction
Training STEM Educators
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