Craig Tovey is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He also co-directs CBID, the Georgia Tech Center for Biologically Inspired Design. 

Dr. Tovey's principal research and teaching activities are in operations research and its interdisciplinary applications to social and natural systems, with emphasis on sustainability, the environment, and energy. His current research concerns inverse optimization for electric grid management, classical and biomimetic algorithms for robots and webhosting, the behavior of animal groups, sustainability measurement, and political polarization.  

Dr. Tovey received a Presidential Young Investigator Award in 1985 and the 1989 Jacob Wolfowitz Prize for research in heuristics. He was granted a Senior Research Associateship from the National Research Council in 1990, was named an Institute Fellow at Georgia Tech in 1994, and received the Class of 1934 Outstanding Interdisciplinary Activity Award in 2011. In 2016, Dr. Tovey was recognized by the ACM Special Interest Group on Electronic Commerce with the Test of Time Award for his work as co-author of the paper “How Hard Is It to Control an Election?” He was a 2016 Golden Goose Award recipient for his role on an interdisciplinary team that studied honey bee foraging behavior which led to the development of the Honey Bee Algorithm to allocate shared webservers to internet traffic. 

Dr. Tovey received an A.B. in applied mathematics from Harvard College in 1977 and both an M.S. in computer science and a Ph.D. in operations research from Stanford University in 1981. 

Chao Zhang is an Assistant Professor at the School of Computational Science and Engineering, Georgia Institute of Technology. His research area is data mining, machine learning, and natural language processing. His research aims to enable machines to understand text data in more label-efficient and robust way in open-world settings. Specific research topics include weakly-supervised learning, out-of-distribution generalization, interpretable machine learning, and knowledge extraction and reasoning. He is a recipient of Google Faculty Research Award, Amazon AWA Machine Learning Research Award, ACM SIGKDD Dissertation Runner-up Award, IMWUT distinguished paper award, and ECML/PKDD Best Student Paper Runner-up Award. Before joining Georgia Tech, he obtained his Ph.D. degree in Computer Science from University of Illinois at Urbana-Champaign in 2018.

Matt is a former Tar Heel from UNC Chapel Hill. His training is in mass spectrometry-based proteomics and G protein signaling. He has been investigating PTMs since 2001. He is also a co-director of the Systems Mass Spectrometry Core (SYMS-C) facility at Georgia Tech.