Projects

The Silent Emergency - Predicting Preterm Birth

Capstone Project for Data Science Boot Camp

Preterm birth is a primary cause of infant mortality and morbidity in the United States, affecting approximately 1 in 10 births. The rates are notably higher among Black women (14.6%), compared to White (9.4%) and Hispanic women (10.1%). Despite its prevalence, predicting preterm birth remains challenging due to its multifaceted etiology rooted in environmental, biological, genetic, and behavioral interactions. Our project harnesses machine learning techniques to predict preterm birth using electronic health records. This data intersects with social determinants of health, reflecting some of the interactions contributing to preterm birth. Recognizing that under-representation in healthcare research perpetuates racial and ethnic health disparities, we take care to use diverse data to ensure equitable model performance across underrepresented populations.

Project page link (includes 5 min summary video)   GitHub link

Umpiring in the Age of Technology - A Study of Pitch Calling by MLB Umpires

In 2024, the pitch calling abilities of home plate umpires are under more scrutiny than ever: entities like Ump Scorecards publish summaries of home plate umpire performance for every Major League Baseball (MLB) game on social media and all AAA-level games in Minor League Baseball (MiLB) used the automated ball-strike (ABS) challenge system (as of June 25th). Given the importance of correct calls to both game outcomes and fans - as well as the looming possibility of an ABS system in MLB games - we investigate both:

1. the efficacy of machine learning models to predict umpires' ball/strike calls, and 

2. what features most impact calling balls and strikes (outside of pitch location).

GitHub link