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Dr. Doostan's research team is focused on the development of novel theories and numerical tools to rigorously tackle several grand challenges associated with Uncertainty Quantification (UQ) and Verification and Validation (V&V) of complex engineering systems. At the core of their work are scalable model reduction approaches, centering on sparse and low-rank approximation techniques, for the propagation of uncertainty though systems with high-dimensional random inputs. The group is currently interested in predictive simulation of coupled electro-chemical phenomenon in Lithium-ion batteries, chemical kinetics in reactive flows, as well as a number of other applications in solid and fluid mechanics. 

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