Merritt Smith is a PhD student at the UC Berkeley School of Information, advised by Joshua Blumenstock. His research lies at the intersection of development economics and machine learning, using digital trace data, household surveys, and satellite imagery to measure welfare in low- and middle-income countries and to evaluate the data-driven systems increasingly used to allocate social protection and humanitarian aid. Current projects detect and characterize long-term poverty dynamics, evaluate the use of large language models in poverty targeting, and develop new methods for extracting welfare-predictive signals from digital trace data. Prior to Berkeley, he was a data scientist at the University of Chicago Crime Lab and the Center for Applied Artificial Intelligence. He holds a Master's in Computational Analysis and Public Policy from the University of Chicago and a BA in Data Science and Public Policy from Tufts University.