C. F. Jeff Wu is the Coca-Cola Chair in Engineering Statistics and Professor in the H. Milton School of Industrial and Systems Engineering at Georgia Tech.

He was elected a Member of the National Academy of Engineering (2004), and a Member (Academician) of Academia Sinica (2000). A Fellow of the Institute of Mathematical Statistics (1984), the American Statistical Association (1985), the American Society for Quality (2002), and the Institute for Operations Research and Management Sciences (2009). He received the COPSS (Committee of Presidents of Statistical Societies) Presidents' Award in 1987, which was given to the best researcher under the age of 40 per year and was commissioned by five statistical societies. His other major awards include the 2011 COPSS Fisher Lecture, the 2012 Deming Lecture (plenary lectures during the annual Joint Statistical Meetings), the Shewhart Medal (2008) from ASQ, and the Pan Wenyuan Technology Award (2008). In 2016 he received the (inaugural) Akaike Memorial Lecture Award. In 2017 he received the George Box Medal from ENBIS. In 2020 he won The Class of 1934 Distinguished Professor Award and the Sigma Xi Monie A. Ferst Award both at Georgia Institute of Technology. He has won numerous other awards, including the Wilcoxon Prize, the Brumbaugh Award (twice), the Jack Youden Prize (twice), and the Honoree of the 2008 Quality and Productivity Research Conference. He was the 1998 P. C. Mahalanobis Memorial Lecturer at the Indian Statistical Institutes and an Einstein Visiting Professor at the Chinese Academy of Sciences (CAS). He is an Honorary Professor at several institutions, including the CAS and National Tsinghua University. He received an honorary doctor (honoris causa) of mathematics at the University of Waterloo in 2008.

He was formerly the H. C. Carver Professor of Statistics and Professor of Industrial and Operations Engineering at the University of Michigan, 1993-2003 and the GM/NSERC Chair in Quality and Productivity at the University of Waterloo in 1988-1993. In his 1997 inaugural lecture for the Carver Chair, he coined the term data science and advocated that statistics be renamed data science and statistician to data scientist. Before Waterloo, he taught in the Statistics Department at the University of Wisconsin from 1977-1988. He got his BS in Mathematics from National Taiwan University in 1971 and Ph.D. in Statistics from the University of California, Berkeley (1973-1976).

His work is widely cited in professional journals as well as in magazines, including a feature article about his work in Canadian Business and a special issue of Newsweek on quality. He has served as editor or associate editor for several major statistical journals like Annals of Statistics, Journal of American Statistical Association, Technometrics, and Statistica Sinica. Professor Wu has published more than 185 research articles in peer review journals. He has supervised 50 Ph.D.'s, out of which more than 25 are teaching in major research departments or institutions in statistics, engineering, or business in US/Canada/Asia/Europe. Among them, there are 21 Fellows of ASA, IMS, ASQ, IAQ and IIE, three editors of Technometrics, and one editor of JQT. He co-authors with Mike Hamada the book "Experiments: Planning, Analysis, and Optimization" (Wiley, 2nd Ed, 2009, 716 pages) and with R. Mukerjee the book "A Modern Theory of Factorial Designs" (Springer, 2006).

Jing Li is a Virginia C. and Joseph C. Mello Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering and a core faculty in the Center for Machine Learning at Georgia Tech. Prior to joining Georgia Tech in 2020, she was a Professor at Arizona State University and is a co-founder of the ASU-Mayo Clinic Center for Innovative Imaging.

Dr. Li’s research develops statistical machine learning algorithms for modeling and inference of complex-structured datasets with high dimensionality (e.g., 3D/4D images), multi-modality, and  heterogeneity. The objectives of the methodological developments are to provide capacities for monitoring & change detection, diagnosis, and prediction & prognosis. The application domains mainly include health and medicine, focusing on medical image data analytics as well as fusion of images, genomics, and clinical records for personalized and precision medicine. Her research outcomes support clinical decision making for diagnosis, prognosis, and telemedicine for various conditions affecting the brain, such as brain cancer, post-traumatic headache & migraine, traumatic brain injury, and the Alzheimer’s disease. Her research received Best Paper awards from various professional venues such as IISE Transactions, IISE Annual Conferences, INFORMS Data Mining and Decision Analytics, American Academy of Neurology, America Headache Society, etc. Her research has been funded by the NIH, NSF, DOD, and industries. She is an NSF CAREER Awardee.

Dr. Li is a former Chair for the Data Mining Subdivision of INFORMS. She is currently a Senior Editor for IEEE Transactions on Automation Science and Engineering and a Department Editor for IISE Transactions on Healthcare Systems Engineering.

Nick Sahinidis is the Butler Family Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering and the School of Chemical and Biomolecular Engineering at Georgia Tech. His current research activities are at the interface between computer science and operations research, with applications in various engineering and scientific areas, including: global optimization of mixed-integer nonlinear programs: theory, algorithms, and software; informatics problems in chemistry and biology; process and energy systems engineering. Sahinidis has served on the editorial boards of many leading journals and in various positions within AIChE (American Institute of Chemical Engineers). He has also served on numerous positions within INFORMS (Institute for Operations Research and the Management Sciences), including Chair of the INFORMS Optimization Society. He received an NSF CAREER award, the INFORMS Computing Society Prize, the MOS Beale-Orchard-Hays Prize, the Computing in Chemical Engineering Award, the Constantin Carathéodory Prize, and the National Award and Gold Medal from the Hellenic Operational Research Society. Sahinidis is a member of the U.S. National Academy of Engineering and a fellow of AIChE and INFORMS.

Turgay Ayer is the Virginia C. and Joseph C. Mello Chair and a professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Ayer also serves as the research director for healthcare analytics and business intelligence in the Center for Health & Humanitarian Systems at Georgia Tech and holds a courtesy appointment at Emory Medical School.

His research focuses on healthcare analytics and socially responsible business analytics with a particular emphasis on practice-focused research. His research papers have been published in top tier management, engineering, and medical journals, and covered by popular media outlets, including the Wall Street Journal, Washington Post, U.S. News, and NPR.

Ayer has received over $2.5 million grant funding and several awards for his work, including an NSF CAREER Award (2015), first place in MSOM Responsible Research in Operations Management (2019), first place in the MSOM Best Practice-Based Research Competition (2017), INFORMS Franz Edelman Laureate Award (2017), and Society for Medical Decision Making Lee Lusted Award (2009).

Ayer serves an associate editor for Operations Research, Management Science, and MSOM, and is a past president of the INFORMS Health Application Society. He received a B.S. in industrial engineering from Sabanci University in Istanbul, Turkey, and his M.S. and Ph.D. degrees in industrial and systems engineering from the University of Wisconsin–Madison.

Yajun Mei is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.

Dr. Mei's research interests include change-point problems and sequential analysis in Mathematical Statistics; sensor networks and information theory in Engineering; as well as longitudinal data analysis, random effects models, and clinical trials in Biostatistics. 

He received a B.S. in Mathematics from Peking University in P.R. China, and a Ph.D. in Mathematics with a minor in Electrical Engineering from the California Institute of Technology. He has also worked as a postdoc in Biostatistics for two years in the Fred Hutchinson Cancer Research Center in Seattle, WA.

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).

Dr. Yu Ding is the Anderson-Interface Chair and Professor in the H. Milton School of Industrial and Systems Engineering at Georgia Tech. Prior to joining Georgia Tech in 2023, he was the Mike and Sugar Barnes Professor of Industrial and Systems Engineering at Texas A&M University. While at Texas A&M, he also served as Associate Department Head for Graduate Affairs of the Department of Industrial and Systems Engineering between 2012 and 2016 and Associate Director for Research Engagement of Texas A&M Institute of Data Science between 2020 and 2023. He received his B.S. in Precision Engineering from the University of Science and technology of China in 1993, a M.S. in Precision Engineering from Tsinghua University in 1996, a second M.S. in Mechanical from Penn State in 1998, and his Ph.D. in Mechanical Engineering from the University of Michigan in 2001.

Dr. Ding is the author of the CRC Press book, Data Science for Wind Energy, and a co-author of the Springer Nature book, Data Science for Nano Image Analysis. Dr. Ding received the 2019 IISE Technical Innovation Award and 2022 INFORMS Impact Prize for his data science innovations impacting wind energy applications. Dr. Ding is a Fellow of IISE (2015) and ASME (2016). He has served as editor or associate editor for several major engineering data science journals, and is currently serving as the 14th Editor in Chief of IISE Transactions, for the term of 2021-2024.