I am currently a Ph.D. candidate in Applied Economics at Texas Tech University. Before that, I completed my undergrad majoring in Finance and Mathematics.
My research develops programmable computational models to tackle challenges that arise from real-world data. I leverage large, high-resolution data sources - microdata, mobile GPS, remote sensing, geospatial gridded data - to capture and improve understanding of social networks and resource-use behavior.
As an applied microeconomist, my research strengthens the data-policy pathway by using applied econometric and machine learning methods, first by collecting unique, high-resolution data, and then by applying advanced techniques that require these high quality, high-resolution data. My work so far has explored areas of spillover effect of conflict (Food Policy'23), causal effects of conflict (Agriculture & Food Security'23), social networks and small-world network outcomes, property taxation discrimination, housing submarkets, socio-economic and locational determinants of food stores using ML methods and causal inference using ML. To address these issues, I have employed research methods ranging from regular econometric and optimization modeling to machine learning, bayesian simulation, network modeling, and geospatial analysis.