Dr. Kostka is currently a professor of Biology at Georgia Institute of Technology (GT). Prior to GT, he was an Associate Professor at the Department of Oceanography, Florida State University. His research involves microorganism studies in geochemical cycles of pristine and contaminated ecosystems, from the oceans to the terrestrial subsurface.

My research integrates my work in complex fluids and granular media and the biomechanics of locomotion of organisms and robots to address problems in nonequilibrium systems that involve interaction of matter with complex media. For example, how do organisms like lizards, crabs, and cockroaches cope with locomotion on complex terrestrial substrates (e.g. sand, bark, leaves, and grass).

Fedorov's background is in thermal/fluid sciences, chemical reaction engineering as well as in applied mathematics. His laboratory works at the intersection between mechanical and chemical engineering and solid state physics and analytical chemistry with the focus on portable/ distributed power generation with synergetic CO₂ capture; thermal management of high power dissipation devices and electronics cooling; special surfaces and nanostructured interfaces for catalysis, heat and moisture management; and development of novel bioanalytical instrumentation and chemical sensors.

For more than a decade, Constantine Dovrolis has been exploring the evolution of our interconnected world. Dovrolis serves as a Professor in the School of Computer Science, College of Computing at the Georgia Institute of Technology and is an affiliate of the Institute for Information Security & Privacy.

Sam Brown's lab studies the multi-scale dynamics of infectious disease. Their goal is to improve the treatment and control of infectious diseases through a multi-scale understanding of microbial interactions. Their approach is highly interdisciplinary, combining theory and experiment, evolution, ecology and molecular microbiology in order to understand and control the multi-scale dynamics of bacteria pathogens.

Dr. Borodovsky and his group develop machine learning algorithms for computational analysis of biological sequences: DNA, RNA and proteins. Our primary focus is on prediction of protein-coding genes and regulatory sites in genomic DNA. Probabilistic models play an important role in the algorithm framework, given the probabilistic nature of biological sequence evolution.