My project develops scalable methods for turning complex farmer, weather, and crop-yield data into practical weather-indexed insurance products. Rather than treating each simulated farmer outcome as its own contract, I use clustering, optimization, and probability-based methods to group similar risk profiles in a way that is computationally efficient and economically meaningful. The goal is to help design insurance systems that are both technically scalable and useful for protecting farmers in developing economies from weather-related risk.

I am taking part in SuperUROP because this project combines my interests – math, computation, economics, and their real-world effects. Because I have already spent time working on this research, I consider SuperUROP my chance to take more ownership on the project, to further develop the methods, explain the technical concepts more clearly, and build the research ideas into a more complete and robust contribution to research. I really look forward to designing more than just interesting, from a theoretical standpoint, research tools, but more importantly, tools to be help redesign financial systems so that they are more resilient and accessible to people facing uncertainty.