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Andrew Allman is an Assistant Professor in the Department of Chemical Engineering at the University of Michigan. His research focuses on process systems engineering, particularly in developing new theories and computational tools that enhance decision-making in chemical process engineering. He aims to find optimal solutions that ensure chemical systems are designed, operated, and controlled in an economical, safe, and sustainable manner. Allman’s team specializes in identifying and exploiting the structural sparsity inherent in mathematical models across chemical, energy, and biological systems. Current theoretical interests include the use of network theory to identify easy-to-solve subproblems within large optimization challenges and reducing the dimensionality of many-objective optimization problems that incorporate sustainability objectives. Allman is also exploring the applications of machine learning to accelerate solution optimization, especially for operational control issues. His research is aimed at tackling challenges in sustainable engineering, plant-wide control, and supply chain management, heralding a new approach towards the future of energy production and manufacturing facilities.
University of Michigan • Ann Arbor, Michigan
Teaching and conducting research in Chemical Engineering.
University of Minnesota, Twin Cities • Twin Cities, Minnesota
Worked under advisor Qi Zhang, focusing on research projects in Chemical Engineering.
Department of Electrical Engineering and Computer Science