Dr. Justin Romberg is the Schlumberger Professor and the Associate Chair for Research in the School of Electrical and Computer Engineering and the Associate Director for the Center for Machine Learning at Georgia Tech.

Dr. Romberg received the B.S.E.E. (1997), M.S. (1999) and Ph.D. (2004) degrees from Rice University in Houston, Texas. From Fall 2003 until Fall 2006, he was a Postdoctoral Scholar in Applied and Computational Mathematics at the California Institute of Technology. He spent the Summer of 2000 as a researcher at Xerox PARC, the Fall of 2003 as a visitor at the Laboratoire Jacques-Louis Lions in Paris, and the Fall of 2004 as a Fellow at UCLA's Institute for Pure and Applied Mathematics. In the Fall of 2006, he joined the Georgia Tech ECE faculty. In 2008 he received an ONR Young Investigator Award, in 2009 he received a PECASE award and a Packard Fellowship, and in 2010 he was named a Rice University Outstanding Young Engineering Alumnus. He is currently on the editorial board for the SIAM Journal on the Mathematics of Data Science, and is a Fellow of the IEEE.

His research interests lie on the intersection of signal processing, machine learning, optimization, and applied probability.

Edwin Romeijn is the H. Milton and Carolyn J. Stewart School Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.

His areas of expertise include optimization theory and applications. His recent research activities deal with issues arising in radiation therapy treatment planning and supply chain management. In radiation therapy treatment planning, his main goal has been to develop new models and algorithms for efficiently determining effective treatment plans for cancer patients who are treated using radiation therapy, and treatment schedules for radiation therapy clinics. In supply chain optimization, his main interests are in the integrated optimization of production, inventory, and transportation processes, in particular in the presence of demand flexibility, limited resources, perishability, and uncertainty.

He previously served as Program Director for the Manufacturing Enterprise Systems, Service Enterprise Systems, and Operations Research programs at the National Science Foundation, and as Professor and Richard C. Wilson Faculty Scholar in the Department of Industrial and Operations Engineering at the University of Michigan. Before joining the University of Michigan in 2008, he was on the faculty of the Department of Industrial and Systems Engineering at the University of Florida and the Rotterdam School of Management at the Erasmus University Rotterdam in The Netherlands. 

He is a Fellow of the Institute of Operations Research and the Management Sciences (INFORMS) and the Institute of Industrial & Systems Engineers (IISE), and a member of the Mathematical Optimization Society (MOS), Society of Industrial and Applied Mathematics (SIAM), and the American Association of Physicists in Medicine (AAPM).

Dana Randall is an American computer scientist. She works as the ADVANCE Professor of Computing, and adjunct professor of mathematics at the Georgia Institute of Technology. She is also an External Professor of the Santa Fe Institute. Previously she was executive director of the Georgia Tech Institute of Data Engineering and Science (IDEaS) that she co-founded, and director of the Algorithms and Randomness Center. Her research include combinatorics, computational aspects of statistical mechanics, Monte Carlo stimulation of Markov chains, and randomized algorithms.

Srinivas Aluru is executive director of the Institute for Data Engineering and Science (IDEaS) and professor in the School of Computational Science and Engineering at Georgia Institute of Technology. He co-leads the NSF South Big Data Regional Innovation Hub which nurtures big data partnerships between organizations in the 16 Southern States and Washington D.C., and the NSF Transdisciplinary Research Institute for Advancing Data Science. Aluru conducts research in high performance computing, large-scale data analysis, bioinformatics and systems biology, combinatorial scientific computing, and applied algorithms. An early pioneer in big data, Aluru led one of the eight inaugural mid-scale NSF-NIH Big Data projects awarded in the first round of federal big data investments in 2012. He has contributed to NITRD and OSTP led white house workshops, and NSF and DOE led efforts to create and nurture research in big data and exascale computing. He is a recipient of the NSF Career award, IBM faculty award, Swarnajayanti Fellowship from the Government of India, the John. V. Atanasoff Discovery Award from Iowa State University, and the Outstanding Senior Faculty Research Award, Dean's award for faculty excellence, and the Outstanding Research Program Development Award at Georgia Tech. He is a Fellow of AAAS, IEEE, and SIAM, and is a recipient of the IEEE Computer Society Golden Core and Meritorious Service awards.