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Ricardo Baptista's research is motivated by the need for accurate uncertainty quantification in complex physical systems. He is particularly interested in developing scalable computational methods for Bayesian inference and probabilistic modeling, with a focus on utilizing measure transport and dimension reduction techniques. His work is practically applied to enhance predictions and build insights into problems across various fields, including science, engineering, and medicine.
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