Mateo Dulce Rubio

¡Hola! I am a Ph.D. candidate in Statistics and Public Policy at Carnegie Mellon University, advised by Edward H. Kennedy. My research focuses on developing flexible and robust statistical methods for humanitarian and policy applications, using tools from nonparametric statistics, causal inference, and responsible machine learning. My work includes the creation of an AI-informed tool for estimating landmine contamination risk, which is being used in Colombia and Afghanistan to identify priority areas for mine clearance in collaboration with the UN Mine Action Service. I also work on developing novel doubly robust estimators for causal inference and population size estimation from capture-recapture data under list dependence and capture heterogeneity.

I am a K&L Gates Presidential Fellow in Ethics and Computational Technologies. I have worked at RAND Corporation and Apple on natural language processing and foundation models. Before that, I co-founded the Centro de Analítica para Políticas Públicas and worked at Quantil mostly on crime modeling research in Bogotá. My background is in Mathematics (BSc) and Economics (BSc, MSc cum laude) from Universidad de los Andes in Colombia.