Georgia Tech School of Chemical and Biomolecular Engineering

Saad Bhamla


 

Research Keywords:

soft matter, low-cost tools, organismic biophysics, fluid dynamics

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Lily S. Cheung


 

Research Keywords:

Systems and Synthetic Biology

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The application of quantitative techniques and mathematical modeling to plants in order to gain systems-level insight into their physiology and development, particularly to understanding how metabolic and gene regulatory networks interact to control growth.

Mark Styczynski


 

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Systems biology, bioinformatics, metabolism, and metabolomics

Hang Lu


 

Research Keywords:

Microfluidics; bioMEMS; behavior neuroscience; cell biology; automation and high throughput engineering approaches to biology and biotechnology, Regenerative Medicine

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Research Interests:

Microfluidics; bioMEMS; behavior neuroscience; cell biology; automation and high throughput engineering approaches to biology and biotechnology

Athanassios Sambanis


 

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Cellular and tissue engineering; genetic engineering of cells for tissue engineering applications; cell encapsulation; magnetic resonance monitoring of tissue constructs; cryopreservation; mathematical modeling of cells and tissues.

Mark Prausnitz


 

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Drug, gene and vaccine delivery; transdermal delivery; ocular delivery; intracellular delivery; microneedles; microfabricated devices; ultrasound

Andreas Bommarius


 

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Biomolecular engineering, especially biocatalysis, biotransformations, and biocatalyst stability

Martha Grover


 

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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.

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