Dr. Doostan's research is focused on data-driven modeling, machine learning, and uncertainty quantification of complex systems. Of particular interest to his work is the development of data-driven and 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, data-driven modeling, and big data compression research.
