Hi! I’m Ashton, an incoming Quantitative and Computational Finance master’s student at Georgia Tech, where I am finishing my B.S. in Industrial Engineering with a concentration in Analytics and Data Science. My work sits at the intersection of machine learning, stochastic modeling, and quantitative risk analysis, with applications in financial systems and large-scale optimization.

Previously, I interned at Los Alamos National Laboratory, where I developed probabilistic models for real-time nuclear asset tracking and built quantitative risk metrics to support national security operations. At Georgia Tech, I co-lead a research team developing stochastic supply chain models that integrate economic and operational data to forecast risk propagation across U.S. energy networks.

My projects often bridge research and practice, from designing machine learning-driven entropic risk optimization frameworks for portfolio construction to developing predictive models for ETF trade profitability using statistical learning techniques. I’m extremely passionate about building data-driven systems that enhance decision-making under uncertainty.

When I’m not modeling data, you’ll probably find me watching college football, exploring the Blue Ridge Mountains, or playing golf.