Allman, Andrew

Professor Andrew Allman’s research team focuses on identifying and exploiting the structure and sparsity inherent in the mathematical models underlying chemical, energy, and biological systems to enable computationally efficient decision making. Current theoretical areas of interest include (1) identifying easy-to-solve subproblems within large, complex optimization problems, (2) developing new solution approaches which better exploit a given problem’s structure, (3) enhancing data-driven decision making methods through a priori dimensionality reduction in data collection, and (4) reducing the complexity of many-objective optimization problems by identifying subsets of objectives which have the strongest tradeoffs.