About Me

I moved to the United States from Australia at the age of 18 to pursue a lifelong dream of combining my footballing abilities & commitment to excellence in the classroom. I was a collegiate athlete at Seton Hill University from 2021-2025, where I was captain of the Men's Soccer team, while pursuing a degree in Data Science. The program took an interdisciplinary approach in exploring the intersection of mathematics, computer science and statistics while fostering a liberal arts curriculum. In my Senior year I realized that I wanted to enter the world of Quantitative Finance and have been working towards this goal ever since. I am working on publishing a research paper using the results from my quantitative finance research project as part of my senior capstone project. I will begin my masters degree in Fall 2025, where I look to further my research and contribute to the field of quantatitive finance, harnessing an interdisciplinary approach rooted in mathematical principles.

"The way you do one thing is the way you do everything" - Martha Beck

Experience

Data Analytics Marketing Intern - CMMB

Education

Seton Hill University

Bachelor of Science major in Data Science

Class of 2025 | GPA: 3.70

Stony Brook University

Master's of Applied Mathematics and Statistics

Starting Fall 2025

Explore My

Recent Research

Predicting Urban Heat Islands in New York City

Urban Heat Islands (UHIs) pose a significant challenge to major cities worldwide, with temperature disparities reaching upwards of ten degrees Celsius. These extreme heat differences contribute to severe health complications, disproportionately affecting vulnerable populations.

Jonah Zembower and I took on the task of building a regression model through a variety of open data sources such as NYC Open Data and Satellite Imagery to build a predictive Machine Learning Algorithm. Through rigorous evaluation of various machine learning algorithms and hyperparameter optimization, we built a robust model capable of predicting urban temperatures with a 0.93 R² score.

This research not only highlights the potential of AI-driven urban climate analysis but also provides a foundation for city planners and policymakers to implement targeted heat mitigation strategies.

Enhancing Technical Analysis with Data Science

Discover how data science methodologies such as parameter optimization and monte carlo simulations can improve the performance of Technical Analysis (TA) techniques. Backtesting technical analysis strategies across different macroeconomic cycles while utilizing sector Exchanged Traded Funds (ETFs) produces a robust understanding of TA performance and economic cycles. It was found that TA struggled to outperform baseline buy & hold strategies in periods of strong economic growth. However it was clear that during vola economic periods, TA outperformed buy & hold strategies, upwards of 10%.

Research Interests

Data Science Methodologies

Reinforcement Learning

Stochastic Modeling

Markov Decision Processes

Machine Learning Engineering

Multivariate Analysis

Statistical Learning

Finance & Mathematics

Partial Derivatives

Fourier Analysis

Probability & Statistics Theory

Stochastic Calculus

Derivative & Asset Pricing

Technical & Fundamental Analysis

Browse My Recent

Projects

Comprehensive Analysis of Maryland Automotive Industry

Assessing the Predictability of Life Expectancy

Benjamin Nicholson