Ricardo Baptista's research is motivated by the need for accurate uncertainty quantification in complex physical systems. He is interested in developing scalable computational methods for Bayesian inference and probabilistic modeling, in particular using measure transport and dimension reduction techniques. Practically, he applies his work to improve predictions and build insights for problems in science, engineering, and medicine.