Valerie Thomas is the Anderson-Interface Chair of Natural Systems and Professor in the H. Milton School of Industrial and Systems Engineering, with a joint appointment in the School of Public Policy. 

Dr. Thomas's research interests are energy and materials efficiency, sustainability, industrial ecology, technology assessment, international security, and science and technology policy. Current research projects include low carbon transportation fuels, carbon capture, building construction, and electricity system development. Dr. Thomas is a Fellow of the American Association for the Advancement of Science, and of the American Physical Society. She has been an American Physical Society Congressional Science Fellow, a Member of the U.S. EPA Science Advisory Board, and a Member of the USDA/DOE Biomass Research and Development Technical Advisory Committee. 

She has worked at Princeton University in the Princeton Environmental Institute and in the Center for Energy and Environmental Studies, and at Carnegie Mellon University in the Department of Engineering and Public Policy.

Dr. Thomas received a B. A. in physics from Swarthmore College and a Ph.D. in theoretical physics from Cornell University.

Nicoleta Serban is the Peterson Professor of Pediatric Research in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.

Dr. Serban's most recent research focuses on model-based data mining for functional data, spatio-temporal data with applications to industrial economics with a focus on service distribution and nonparametric statistical methods motivated by recent applications from proteomics and genomics. 

She received her B.S. in Mathematics and an M.S. in Theoretical Statistics and Stochastic Processes from the University of Bucharest. She went on to earn her Ph.D. in Statistics at Carnegie Mellon University.

Dr. Serban's research interests on Health Analytics span various dimensions including large-scale data representation with a focus on processing patient-level health information into data features dictated by various considerations, such as data-generation process and data sparsity; machine learning and statistical modeling to acquire knowledge from a compilation of health-related datasets with a focus on geographic and temporal variations; and integration of statistical estIMaTes into informed decision making in healthcare delivery and into managing the complexity of the healthcare system.

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.

George Lan is an A. Russell Chandler III Professor of Industrial and Systems Engineering at Georgia Institute of Technology.  His research and teaching interests lie in theory, algorithms and applications of stochastic optimization and nonlinear programming.  Most of his current research concerns the design of efficient algorithms for solving challenging optimization problems, especially those arising from data analytics, machine learning, and reinforcement learning. He is actively pursuing the applications of these methodologies in healthcare and sustainability areas. Dr. Lan serves as the associate editor for Computational Optimization and Applications (2014 – present), Mathematical Programming (2016 – present) and SIAM Journal on Optimization (2016  – present). Dr. Lan is an associate director for the center of machine learning at Georgia Tech.

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.

Mohsen Moghaddam is the Gary C. Butler Family Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering and the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technology. He directs the Symbiotic and Augmented Intelligence Lab (SAIL), where his research focuses on developing human-centered computational models, algorithms, and tools at the intersection of AI and spatial computing to enhance learning and creativity in various cognitive and psychomotor tasks within industrial settings. Previously, Dr. Moghaddam was an Assistant Professor in the Department of Mechanical and Industrial Engineering and an Affiliated Faculty with the Khoury College of Computer Sciences at Northeastern University in Boston. He has also served as a Visiting Professor with the HumanTech project at Politecnico di Milano and as a Visiting Scholar at the Next Level Lab, Harvard University. Dr. Moghaddam earned his PhD in Industrial Engineering from Purdue University and completed a Postdoctoral Associate position at the GE-Purdue Partnership in Research and Innovation in Advanced Manufacturing. His research has been supported by the U.S. National Science Foundation, the U.S. Defense Advanced Research Projects Agency, the U.S. Navy, and industry partners.

Sen Na is an Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Tech. Prior to joining ISyE, he was a postdoctoral researcher in the Department of Statistics and the International Computer Science Institute (ICSI) at University of California, Berkeley, working with Michael W. Mahoney. He received his Ph.D. in statistics from the University of Chicago in 2021, under the supervision of Mihai Anitescu and Mladen Kolar. Before attending University of Chicago, he received his B.S. in mathematics from Nanjing University in 2016. 

Na is broadly interested in the mathematical foundations of data science, with topics including high-dimensional statistics, graphical models, semiparametric models, optimal control, and large-scale and stochastic nonlinear optimization. He is also interested in the broad applications of machine learning methods in biology, neuroscience, and engineering. 

Na has received multiple awards, including the prestigious William Rainey Harper Dissertation Fellowship from UChicago and the 2023 MAPR Meritorious Service Award from the Mathematical Optimization Society.

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).