Embedded Files
Baseball research
Under the mentorship of NC State's Dr. Osborne, a team of two other students and I set out to find a competitive edge that MLB teams could take advantage of. Our research focused on the benefit of utilizing the double steal, a tactic where, in instances where a team has runners on first and second base (but not third), both players attempt to steal a base at once. We found that it increased the average number of runs outputted per team by over 33% when employed. Our poster below was presented at the NC State Undergraduate Research Symposium in the spring of 2025. This was my first research experience, and it was a great opportunity to practice managing and incorporating different viewpoints, as we collaborated on an open-ended question with different perspectives.
poster1.pdfSeasonal Streamflow Research and App Development
I assisted NC State's Dr. Schliep with her research on seasonal streamflow patterns across the US. Over the course of 8 months, I developed a Shiny app in R that allows users to interact with data collected from river gages nationwide. This app, alongside the core research, was presented at NC State's Undergraduate Research Symposium in the Spring of 2026. This project allowed me to advance my R programming and UI/UX design abilities, providing practical experience in transforming raw research data into a functional public resource.
Shiny App: https://shiny.stat.ncsu.edu/seasonalstreamflow/
Triangle Sports Competition
In a team of three, my partners and I created a model to numerically predict the outcome of ACC basketball games. We used analysis of variables and a linear regression model to create predictions for the spread of each game. Our model was built using the outcomes of past games in tandem with Torvik performance metrics such as Offensive and Defensive rating. We competed in 2025 and 2026, going head-to-head against teams of students from NC State, UNC, and Duke.
MA 326 NFL Draft Project
In MA 326 (Mathematical Foundations of Data Science), I had the opportunity to participate in an open-ended partner project. My partner and I decided to develop a model that would be used to predict where collegiate football players would get drafted in the NFL draft. We trained our model on past years' data, and tested it against the most recent year. Going forward, we hope to fine-tune the model to uncover underlying attributes that make prospects appealing to NFL front offices.
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