• I'm a Master's in Mathematics student at Miami University, working on my thesis and graduating in December 2026.
• Currently working as a Data Science Intern at Arhaus after spending two years as Director of Analytics with Miami Baseball.
• My work tends to sit somewhere between the intersection of all of the quantitative methods spanning applied mathematics, statistics, machine learning, AI, and just about anything in between.

Data Science Intern, Arhaus (2026)
Built an automated document-ingestion pipeline (PostgreSQL + React) for cross-team data workflows, deployed via Azure and Docker; used Snowflake for graph-based ecommerce basket analysis and NLP-driven customer sentiment modeling.

Director of Analytics & Head Student Manager, Miami University Baseball (2023–2026)
Led a team of 4 student analysts overseeing advanced scouting, pitcher development plans, and pitch design collaboration with coaching staff. Built and deployed R Shiny/Plotly applications for player analysis; developed Stuff+, Location+, and swing-decision models. See Miami University Baseball below for the tooling built during this role.

Money Bull — Mathematical Sports Ranking
With Mia Adler, Ford McDill, and Tiffanie Ng: developed a novel linear-algebraic ranking model for professional rodeo, published in Mathematics and Sports and presented at JMM 2025. Paper

TDA Pitch Clustering — Topological Data Analysis
Applied the Mapper algorithm and clustering techniques to MLB pitch-level data to explore the structure and similarity of different pitch types. Live demo

• Miami University Baseball — Built analytics tools, models, dashboards, and scouting resources using Trackman and other baseball data to support player evaluation and coaching decisions.
• MoneyScore — With Harrison Cradduck, Joey Endres, and Sam Honicky: NHL performance-vs-pay modeling (MoneyScore composite metric, Elo ratings, XGBoost cap-hit prediction), presented at JMM 2026.
• Spotify Taste Graph — Built a graph-based representation of music taste to explore relationships between artists, genres, and listening patterns.

Databases — Cobalt Mineral DB: normalized MySQL schema, functional-dependency/normalization analysis, and stored procedures over USGS mineral-deposit data.

Machine Learning — ML Classifier Benchmark: compared 9 classification algorithms (XGBoost, Random Forest, ANN, and more) on windowed sensor data, with full hyperparameter tuning and cross-validation.

Applied Math — SVD Image Compression and House of Reps TDA: linear algebra and topological methods applied to image compression and congressional voting data — the latter an early precursor to the TDA thesis work above.

Optimization — Schedule Optimization: with Joseph Follrath and Jackson Frey, a mixed-integer linear program (OR-Tools) that builds optimal four-year course schedules under prerequisite, corequisite, and credit-load constraints.