Christine Heitsch is Professor of Mathematics at Georgia Tech, with courtesy appointments in Biological Sciences and Computational Science & Engineering as well as an affiliation with the Petit Institute for Bioengineering & Bioscience.

She is also Director of the new Southeast Center for Mathematics and Biology (SCMB), an NSF-Simons MathBioSys Research Center, and finishing her tenure directing the GT Interdisciplinary Mathematics Preparation and Career Training (IMPACT) Postdoctoral Program.

Heitsch's research interests lie at the interface between discrete mathematics and molecular biology, specifically combinatorial problems "as motivated by" and "with applications to" fundamental biomedical questions like RNA folding.

Students interested in pursuing graduate studies in discrete mathematical biology can do so through a number of GT PhD programs including Bioinformatics or Quantitative Biosciences as well as Algorithms, Combinatorics, and Optimization (ACO), Computational Science & Engineering (CSE), and (of course) Mathematics.
 

Dyer’s research interests lie at the intersection of machine learning, optimization, and neuroscience. Her lab develops computational methods for discovering principles that govern the organization and structure of the brain, as well as methods for integrating multi-modal datasets to reveal the link between neural structure and function.

Josiah Hester works broadly in computer engineering, with a special focus on wearable devices, edge computing, and cyber-physical systems. His Ph.D. work focused on energy harvesting and battery-free devices that failed intermittentently. He now focuses on sustainable approaches to computing, via designing health wearables, interactive devices, and large-scale sensing for conservation. 
   
His work in health is focused on increasing accessibility and lowering the burden of getting preventive and acute healthcare. In both situations, he designs low-burden, high-fidelity wearable devices that monitor aspects of physiology and behavior, and use machine learning techniques to suggest or deliver adaptive and in-situ interventions ranging from pharmacological to behavioral. 
   
His work is supported by multiple grants from the NSF, NIH, and DARPA. He was named a Sloan Fellow in Computer Science and won his NSF CAREER in 2022. He was named one of Popular Science's Brilliant Ten, won the American Indian Science and Engineering Society Most Promising Scientist/Engineer Award, and the 3M Non-tenured Faculty Award in 2021. His work has been featured in the Wall Street Journal, Scientific American, BBC, Popular Science, Communications of the ACM, and the Guinness Book of World Records, among many others.

King Jordan is Professor in the School of Biological Sciences and Director of the Bioinformatics Graduate Program at the Georgia Institute of Technology. He has a computational laboratory and his group works on a wide variety of research and development projects related to: (1) human clinical & population genomics, (2) computational genomics for public health, and (3) computational approaches to functional genomics. He is particularly interested in the relationship between human genetic ancestry and health. His lab is also actively engaged in capacity building efforts in genomics and bioinformatics in Latin America. 

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.

Ramprasad joined the School of Materials Science and Engineering at Georgia Tech in February 2018. Prior to joining Georgia Tech, he was the Centennial Term Professor of Materials Science and Engineering at the University of Connecticut. He joined the University of Connecticut in Fall 2004 after a 6-year stint with Motorola’s R&D laboratories at Tempe, AZ. Ramprasad received his B. Tech. in Metallurgical Engineering at the Indian Institute of Technology, Madras, India, an M.S. degree in Materials Science and Engineering at the Washington State University, and a Ph.D. degree also in Materials Science and Engineering at the University of Illinois, Urbana-Champaign.

Ramprasad’s area of expertise is in the development and utilization of computational and data-driven (machine learning) methods aimed at the design and discovery of new materials. Materials classes under study include polymers, metals and ceramics (mainly dielectrics and catalysts), and application areas include energy production and energy storage. Prof. Ramprasad’s research has been funded by the Office of Naval Research (ONR), the National Science Foundation (NSF), the Department of Energy (DOE), the Army Research Office (ARO), and Toyota Research Institute (TRI). He has lead a ONR-sponsored Multi-disciplinary University Research Initiative (MURI) in the past to accelerate the discovery of polymeric capacitor dielectrics for energy storage, and is presently leading another MURI aimed at the understanding and design of dielectrics tolerant to enormous electric fields.

Ramprasad is a Fellow of the American Physical Society, an elected member of the Connecticut Academy of Science and Engineering, and the recipient of the Alexander von Humboldt Fellowship and the Max Planck Society Fellowship for Distinguished Scientists.

Sholl’s research focuses on materials whose macroscopic dynamic and thermodynamic properties are strongly influenced by their atomic-scale structure. Much of this research involves applying computational techniques such as molecular dynamics, Monte Carlo simulations and quantum chemistry methods to materials of interest. Although the group's work is centered on computational methods, it involves extensive collaboration with experimental groups and industrial partners.