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 develops flexible and robust statistical methods for humanitarian and policy applications, using tools from nonparametric statistics, causal inference, mathematical optimization, and responsible machine learning.

My work includes creating an AI-informed tool for estimating landmine contamination risk, used in Colombia and Afghanistan to identify priority areas for mine clearance in collaboration with the UN Mine Action Service and UNOPS. I also work on developing doubly robust estimators for causal inference and population size estimation under capture heterogeneity and recapture dependence, with applications in conservation, public health, and human rights. Finally, I investigate limitations of current algorithmic decision-making from observational data and devise new methods to address issues like selection biases, missing data, privacy, and algorithmic discrimination.

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.