Dr. Doostan's research is focused on assimilation and propagation of uncertainties in complex PDE-based models for the purpose of model validation and verification. Of particular interest to his work is the development of reduced order models for scalable solution of partial differential equations with random inputs. He is also involved in the development of efficient computational tools for large-scale statistical inverse problems.
