ANDREW BELZ

GitHub LinkedIn pbelz@byu.edu

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Education

Bachelor of Science: Applied & Computational Mathematics

Expected Graduation: April 2028

Brigham Young University, Provo, UT
GPA: 3.93/4.0
Relevant Coursework: Network Theory (Python), Multivariate and Functional Data Analysis (R), Algorithms and Optimization (Python), Multivariable Calculus, Differential Equations, Linear Algebra (Python), Data Structures (C++), Analysis of Variance (R), Applied R Programming (R), Mathematical Analysis (Python).

Bachelor of Science: Data Science (Did not complete degree)

August 2023 - December 2024

Brigham Young University - Idaho, Rexburg, ID
GPA: 4.0/4.0
Relevant Coursework: Intermediate Statistics, Applied Linear Regression (R), Data Science Programming (Python), Introduction to Databases (MySQL).

Relevant Experience

Undergraduate Research Assistant (Python & C++)

Brigham Young University, Provo, UT
Department of Mathematics
May 2025 - Present

Designing and implementing topological community detection methods for complex networks and abstract simplicial complexes.
Engineering algorithms in Python and C++ to efficiently compute and analyze topological characteristics of networks.
Identified statistically significant higher-order structure in functional partitions of the brain of C. elegans.
Presented at BYU’s 2026 Student Research Conference. Abstract: Homology of Colored Graphs: A Framework for Type-Aware Network Topology.
Advised by Dr. Benjamin Webb and Dr. Curtis Kent.

Projects

Persistent Homology of Archaeological Similarity Networks (Python)

June 2026 - Present

Packages and tools: Python, Pandas, NumPy, SciPy, Ripser.
Implemented persistent homology to study archaeological similarity networks (ASNs).
Identified topologically significant differences between geographic topology and cultural topology for sites in the American Southwest.
Preparing manuscript for journal submission.

Graph Dynamical Systems Simulator (Python)

December 2025

Packages and tools: NumPy, NetworkX, Matplotlib.
Architected a simulation framework for discrete dynamical systems on graphs to model non-linear node-state evolution across complex network topologies.
Optimized state-update algorithms utilizing SciPy sparse matrix operations, reducing computational complexity from O(|V|²) to O(|E|).

Competitive Chess Network Analysis (Python)

November 2025

Packages and tools: NumPy, Pandas, sqlite, igraph, NetworkX, Matplotlib.
Engineered an efficient data pipeline using SQLite and Pandas to construct an undirected network model of 500,000 players from 9 million matches.
Implemented community detection algorithms (e.g. greedy modularity maximization) and computed centrality measures and heuristics (e.g., Eigenvector, Betweenness, Core-periphery) using igraph to extract latent player clusters and hierarchies.

Skills

Programming Languages: Python, C++, R, SQL
Methods & Methodologies: Network Analysis, Linear Models, Simulation, Multivariate Statistical Inference, Topological Data Analysis, Functional Data Analysis
Software Tools: Git, LaTeX, MySQL, Markdown