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 (AEA’24, SEA’23, AAEA’23), property taxation discrimination (AEA’24, SEA’23, MCRSA’23), housing submarkets (MCRSA’23), socio-economic and locational determinants of food stores using ML methods (AAEA’23, AEA’23) and causal inference using ML (AAEA’23, AEA’23, AAEA’22). 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.

Publications, current projects, including working papers, can be found here. A copy of my CV can be found here. I can be reached at syed.m.fuad@ttu.edu.

I have teaching experience in statistics and have TA-ed in Ph.D. quantitative methodology in natural resource economics and undergraduate finance courses.