Martha Grover
Professor; School of Chemical & Biomolecular Engineering
Associate Chair for Graduate Studies; School of Chemical & Biomolecular Engineering
James Harris Faculty Fellow; School of Chemical & Biomolecular Engineering
Member; NSF/NASA Center for Chemical Evolution
Dr. Grover’s research activities in process systems engineering focus on understanding macromolecular organization and the emergence of biological function. Discrete atoms and molecules interact to form macromolecules and even larger mesoscale assemblies, ultimately yielding macroscopic structures and properties. A quantitative relationship between the nanoscale discrete interactions and the macroscale properties is required to design, optimize, and control such systems; yet in many applications, predictive models do not exist or are computationally intractable.
The Grover group is dedicated to the development of tractable and practical approaches for the engineering of macroscale behavior via explicit consideration of molecular and atomic scale interactions. We focus on applications involving the kinetics of self-assembly, specifically those in which methods from non-equilibrium statistical mechanics do not provide closed form solutions. General approaches employed include stochastic modeling, model reduction, machine learning, experimental design, robust parameter design, and estimation.
404.894.2878
Office Location:
ES&T 1228
Georgia Institute of Technology
Colloids; Crystallization; Organic and Inorganic Photonics and Electronics; Polymers; Discrete atoms and molecules interact to form macromolecules and even larger mesoscale assemblies, ultIMaTely yielding macroscopic structures and properties. A quantitative relationship between the nanoscale discrete interactions and the macroscale properties is required to design, optimize, and control such systems; yet in many applications, predictive models do not exist or are computationally intractable. The Grover group is dedicated to the development of tractable and practical approaches for the engineering of macroscale behavior via explicit consideration of molecular and atomic scale interactions. We focus on applications involving the kinetics of self-assembly, specific those in which methods from non-equilibrium statistical mechanics do not provide closed form solutions. General approaches employed include stochastic modeling, model reduction, machine learning, experimental design, robust parameter design, estIMaTion, and optimal control, monitoring and control for nuclear waste processing and polymer organic electronics
Research Affiliations: Center for Chemical Evolution
IRI Connection:
