Yonggang Ke's research is highly interdisciplinary combining chemistry, biology, physics, material science, and engineering. The overall mission of his research is to use interdisciplinary research tools to program nucleic-acid-based "beautiful structures and smart devices" at nanoscale, and use them for scientific exploration and technological applications.

We strive to innovate in ways that both advance the imaging science and also impact biological and translational research. We are particularly interested in new imaging physics, bottom-up opto-electronic system design, as well as new principles for light propagation, light-matter interaction and image formation in complex biological materials, especially at the single-molecule level.

Jaydev P. Desai, Ph.D, is currently a Professor and BME Distinguished Faculty Fellow in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech. Prior to joining Georgia Tech in August 2016, he was a Professor in the Department of Mechanical Engineering at the University of Maryland, College Park (UMCP). He completed his undergraduate studies from the Indian Institute of Technology, Bombay, India, in 1993. He received his M.A. in Mathematics in 1997, M.S. and Ph.D.

Edward Botchwey received a B.S. in mathematics from the University of Maryland at College Park in 1993 and both M.E. and Ph.D. degrees in materials science engineering and bioengineering from the University of Pennsylvania in 1998 and 2002 respectively. He was recruited to the faculty at Georgia Tech in 2012 from his previous position at the University of Virginia. His current position is associate professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University. Botchwey is former Ph.D.

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.