Computational Modeling and Machine Learning

Financial Market Behavior Modeling

Applied theoretical physics and stochastic differential equations, incorporating research from Non-parametric estimation of Stochastic Differential Equations from stationary time-series (https://arxiv.org/abs/2007.08054), to develop a computational model for financial market behavior. This enabled improved forecasting of asset price dynamics and volatility.

Strengthened my skills in mathematical modeling, data wrangling, and algorithm implementation, gaining experience in quantitative analysis and stochastic processes.

For any inquiries or references, please reach out to Professor Sarah Marzen - https://www.sarahmarzen.com/

Golf Ball Flight Analysis in Maple & MATLAB

This was a pet project I created to model golf ball flight. I golf a bit and was fiddling around with maple and decided to build a dynamic golf flight simulator with different.

Along find some of the key golf related findings as well as an example ball flight - similar to what I see on the course - on a good day!

Bankruptcy Prediction Algorithms for Partner Loan Providers

Developed and deployed a predictive machine learning model to assess bankruptcy risk for partner loan providers, optimizing financial risk management through data-driven insights.

Solely responsible for coding and implementing the machine learning pipeline, from data preprocessing and feature engineering to model selection, validation, and deployment in a production-ready format.

Gained expertise in financial data analysis, machine learning model development, and cross-functional collaboration, strengthening my ability to build scalable AI-driven solutions for real-world business challenges.

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Bigfoot by the Numbers: A Statistical Hunt for the Mythical Beast

Took a data-driven approach to the Bigfoot mystery, analyzing reported sightings across U.S. counties and building statistical models to predict where the legendary creature is most likely to be found.

Explored the relationship between sightings and socio-economic factors, using Poisson regression, machine learning, and logit models to separate myth from statistical patterns—turns out, Bigfoot loves the Pacific Northwest.

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