Arkadi Nemirovski is the John P. Hunter, Jr. Chair in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. 

Dr. Nemirovski's research interests focus on Optimization Theory and Algorithms, with emphasis on investigating complexity and developing efficient algorithms for nonlinear convex programs, optimization under uncertainty, applications of convex optimization in engineering, and nonparametric statistics. 

Dr. Nemirovski has made fundamental contributions in continuous optimization in the last thirty years that have significantly shaped the field. In recognition of his contributions to convex optimization, Nemirovski was awarded the 1982 Fulkerson Prize from the Mathematical Programming Society and the American Mathematical Society (joint with L. Khachiyan and D. Yudin), the Dantzig Prize from the Mathematical Programming Society and the Society for Industrial and Applied Mathematics in 1991 (joint with M. Grotschel). He was elected a Member of the National Academy of Engineering (2017) and a Fellow of the American Academy of Arts and Sciences (2018). 

In recognition of his seminal and profound contributions to continuous optimization, Nemirovski was awarded the 2003 John von Neumann Theory Prize by the Institute for Operations Research and the Management Sciences (along with Michael Todd). He He continues to make significant contributions in almost all aspects of continuous optimization: complexity, numerical methods, stochastic optimization, and non-parametric statistics. 

Dr. Nemirovski earned a Ph.D. in Mathematics (1974) from Moscow State University, the Doctor of Sciences in Mathematics (1990) from the Supreme Attestation Board at the USSR Council of Ministers, and the Doctor of Mathematics (Honoris Causa) from the University of Waterloo, Canada (2009).

Yao Xie is a Coca-Cola Foundation Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, which she joined in 2013 as an Assistant Professor. She also serves as Associate Director of Machine Learning and Data Science of the Center for Machine Learning. From September 2017 until March 2023 she was the Harold R. and Mary Anne Nash Early Career Professor. She was a Research Scientist at Duke University from 2012 to 2013. 

Her research lies at the intersection of statistics, machine learning, and optimization in providing theoretical guarantees and developing computationally efficient and statistically powerful methods for problems motivated by real-world applications. 

She is currently an Associate Editor for IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing, Journal of the American Statistical Association: Theory and Methods, Sequential Analysis: Design Methods and Applications, INFORMS Journal on Data Science, and an Area Chair of NeurIPS and ICML.

Xiaoming Huo is an A. Russell Chandler III Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Dr. Huo's research interests include statistical theory, statistical computing, and issues related to data analytics. He has made numerous contributions on topics such as sparse representation, wavelets, and statistical problems in detectability. His papers appeared in top journals, and some of them are highly cited. He is a senior member of IEEE since May 2004. 

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).

Kamran Paynabar is the Fouts Family Early Career Professor and Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He received his B.Sc. and M.Sc. in Industrial Engineering from Iran in 2002 and 2004, respectively, and his Ph.D. in Industrial and Operations Engineering from The University of Michigan in 2012. He also holds an M.A. in Statistics from The University of Michigan. His research interests comprise both applied and methodological aspects of machine-learning and statistical modeling integrated with engineering principles. He is a recipient of the INFORMS Data Mining Best Student Paper Award, the Best Application Paper Award from IIE Transactions, the Best QSR refereed paper from INFORMS, and the Best Paper Award from POMS. He has been recognized with the Georgia Tech campus level 2014 CETL/BP Junior Faculty Teaching Excellence Award and the Provost Teaching and Learning Fellowship. He served as the chair of QSR of INFORMS, and the president of QCRE of IISE.

Pinar Keskinocak is the William W. George Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. She is also co-founder and director of the Center for Health and Humanitarian Systems. Previously, she served as the College of Engineering ADVANCE Professor and as interim associate dean for faculty development and scholarship. Prior to joining Georgia Tech, she worked at IBM T.J. Watson Research Center. She received her Ph.D. in Operations Research from Carnegie Mellon University, and her M.S. and B.S. in Industrial Engineering from Bilkent University. 

Dr. Keskinocak's research focuses on the applications of operations research and management science with societal impact, particularly health and humanitarian applications, supply chain management, and logistics/transportation. Her recent work has addressed infectious disease modeling (including Covid-19, malaria, Guinea worm, pandemic flu), evaluating intervention strategies, and resource allocation; catch-up scheduling for vaccinations; hospital operations management; disaster preparedness and response (e.g., prepositioning inventory); debris management; centralized and decentralized price and lead time decisions. She has worked on projects with companies, governmental and non-governmental organizations, and healthcare providers, including American Red Cross, CARE, Carter Center, CDC, Children’s Healthcare of Atlanta, Emory University, and Intel Corporation. 

She is an INFORMS Fellow and currently serves as the president of INFORMS. Previously she served as the Secretary of INFORMS, a department editor for Operations Research (Policy Modeling and Public Sector area), associate editor for Manufacturing & Service Operations Management, and INFORMS Vice President of Membership and Professional Recognition. She is the co-founder and past-president of INFORMS Section on Public Programs, Service, and Needs, and the president of the INFORMS Health Applications Society.

Professor Nagi Gebraeel is the Georgia Power Early Career Professor and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He received his MS and PhD from Purdue University in 1998 and 2003, respectively.

Dr. Gebraeel's research interests lie at the intersection of Predictive Analytics and Machine Learning in IoT enabled maintenance, repair and operations (MRO) and service logistics. His key focus is on developing fundamental statistical learning algorithms specifically tailored for real-time equipment diagnostics and prognostics, and optimization models for subsequent operational and logistical decision-making in IoT ecosystems. Dr. Gebraeel also develops cyber-security algorithms intended to protect IoT-enabled critical assets from ICS-type cyberattacks (cyberattacks that target Industrial Control Systems). From the standpoint of application domains, Dr. Gebraeel has general interests in manufacturing, power generation, and service-type industries. Applications in Deep Space missions are a recent addition to his research interests, specifically, developing Self-Aware Deep Space Habitats through NASA's HOME Space Technology Research Institute.

Dr. Gebraeel leads Predictive Analytics and Intelligent Systems (PAIS) research group at Georgia Tech's Supply Chain and Logistics Institute. He also directs activities and testing at the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute. Formerly, Dr. Gebraeel served as an associate director at Georgia Tech's Strategic Energy Institute (from 2014 until 2019) where he was responsible for identifying and promoting research initiatives and thought-leadership at the intersection of Data Science and Energy applications. He was also the former president of the Institute of Industrial and Systems Engineers (IISE) Quality and Reliability Engineering Division, and is currently a member of the Institute for Operations Research and the Management Sciences (INFORMS), and IISE (since 2005).