Stochastic Optimization and Predictive Analytics in MEDEVAC Policy by Barrett Heritage ’25
Wed, May 7th, 2025
1:00 pm - 1:50 pm
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Stochastic Optimization and Predictive Analytics in MEDEVAC Policy and Swimming Performance by Barrett Heritage ’25, Wednesday May 7, 1:00 – 1:50pm, North Science Building 113, Wachenheim, Mathematics Thesis Defense
My thesis explores stochastic linear programming and machine learning techniques, applying them to two domains: military medical evacuation (MEDEVAC) policy and competitive swimming analysis. In the MEDEVAC context, I discuss tools for optimizing adaptive helicopter dispatch strategies in uncertain, dynamic combat environments. For swimming analysis, I provide a framework for forecasting taper-related performance improvements for college swimmers and how to leverage that information to better predict meet outcomes. These applications illustrate how data and optimization can lead us to better strategies in high-stakes settings.