As a student, Aqua Asberry, HT(ASCP)CM, studied chemistry and was determined to become a forensic scientist. An intuitive mentor steered her towards histology, and Ms. Asberry is currently Research Histology Manager at Parker H. Petit Institute for Bioengineering and Bioscience at Georgia Tech.

Alex Adams’s research focuses on designing, fabricating, and implementing new ubiquitous and wearable sensing systems. In particular, he is interested in how to develop these systems using equity-driven design principles for healthcare. Alex leverages sensing, signal processing, and fabrication techniques to design, deploy, and evaluate novel sensing technologies.

Originally a musician, Alex became fascinated by how he could capture and manipulate sounds through analog hardware and digital signal processing, which led him back to his hometown (Concord, NC). Alex completed his BS at the University of North Carolina at Charlotte in 2014 and his Ph.D. at Cornell University in 2021 (advised by Professor Tanzeem Choudhury). Alex then became the resident Research Scientist for the Precision Behavioral Health Initiative at Cornell Tech (NYC) until the fall of 2022, when he joined the School of Interactive Computing at the Georgia Institute of Technology. Currently, his research focuses on the equity-driven design and the development of multi-modal sensing systems to simultaneously assess mental and physical health to enable a new class of mobile health technologies.

Ratan is an Assistant Professor of Cognition and Brain Science in the School of Psychology at Georgia Tech, and the Director of the Murty Lab (murtylab.com). He obtained his PhD from Indian Institute of Science (IISc) Bangalore and was a postdoctoral researcher in the Kanwisher and DiCarlo labs at MIT before moving to Georgia Tech. Research in the Murty Lab aims to uncover the neural codes and algorithms that enable us to see. The central theme of the lab's work is to integrate biological vision with artificial models of vision. The lab combines the benefits of closed-loop experimental testing (using 3T/7T human functional-MRI) with cutting-edge computational methods (like deep neural networks, generative algorithms, and AI interpretability) toward a new computationally precise understanding of human vision. This research also guides the development of neurally mechanistic biologically constrained models aimed to uncover a better understanding of the neurobiological changes that underlie perceptual abnormalities such as agnosias.