Dr. Jianjun Shi is the Carolyn J. Stewart Chair and Professor in H. Milton Stewart School of Industrial and Systems Engineering, with joint appointment in the George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology. Prior to joining Georgia Tech in 2008, he was the G. Lawton and Louise G. Johnson Professor of Engineering at the University of Michigan. He received his B.S. and M.S. in Electrical Engineering from the Beijing Institute of Technology in 1984 and 1987, and his Ph.D. in Mechanical Engineering from the University of Michigan in 1992. Dr. Shi is a pioneer in the development and application of data fusion for quality improvements. His methodologies integrate system informatics, advanced statistics, and control theory for the design and operational improvements of manufacturing and service systems by fusing engineering systems models with data science methods. He has produced 40 Ph.D. graduates, 27 of which have joined IE department as faculty members. Among them, 7 have received NSF CAREER Awards and one has received the NSF PECASE award. He has published one book and more than 180 papers. He has served as PI and co-PI for projects totaling more than 25 million dollars, which were funded by National Science Foundation, NIST Advanced Technology Program, Department of Energy, General Motors, Daimler-Chrysler, Ford, Boeing, Lockheed-Martin, Honeywell, Pfizer, Samsung, and various other industrial companies and funding agencies. The technologies developed in Dr. Shi’s research group have been widely implemented in various production systems with significant economic impacts. 

Dr. Shi is the founding chair of the Quality, Statistics and Reliability (QSR) Subdivision at the Institute for Operations Research and Management Science (INFORMS). He has served as the Editor-in-Chief of the IISE Transactions (2017-2020), the flagship journal of the Institute of Industrial and Systems Engineers. He also served as the Focus Issue Editor of IISE Transactions on Quality and Reliability Engineering (2007-2017), editor of Journal of System Science and Complexity, and advisory editor of Journal of Quality Technology and Quantitative Management (QTQM). He is a Fellow of American Society of Mechanical Engineering (ASME), a Fellow of the Institute of Industrial and Systems Engineering (IISE), a Fellow of Institute of Operations Research and the Management Science (INFORMS), a Fellow of Society of Manufacturing Engineering (SME), an Academician of the International Academy for Quality, and a member of National Academy of Engineering (NAE) of the USA. 

Dr. Shi received various awards for his research and teaching, including the George Box Medal (2022), ASQ Walter Shewhart Medal (2021), The S. M. Wu Research Implementation Award (2021), ASQ Brumbaugh Award (2019), The Horace Pops Medal Award (2018), IISE David F. Baker Distinguished Research Award (2016), the IIE Albert G. Holzman Distinguished Educator Award (2011), Forging Achievement Award from Forging Industry Educational and Research Foundation (2007), Monroe-Brown Foundation Research Excellence Award (2007), the 1938E Award (1998) at The University of Michigan, and NSF CAREER Award (1996).

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.

Leonid Abramowich Bunimovich (born August 1, 1947) is a Soviet and American mathematician, who made fundamental contributions to the theory of dynamical systems, statistical physics, and various applications.

 Bunimovich received his bachelor's degree in 1967, master's degree in 1969, and Ph.D. in 1973 from the University of Moscow. His masters and Ph.D. thesis advisor was Yakov G. Sinai. 

Bunimovich is a Regents' Professor of mathematics at the Georgia Institute of Technology, a Fellow of the Institute of Physics, and was awarded Humboldt Prize in Physics.

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.

Santosh Vempala is a prominent computer scientist. He is a Distinguished Professor of Computer Science at the Georgia Institute of Technology. His main work has been in the area of Theoretical Computer Science. 

Vempala secured B.Tech. degree in Computer Science and Engineering from Indian Institute of Technology, Delhi, in 1992 then he attended Carnegie Mellon University, where he received his Ph.D. in 1997 under professor Avrim Blum. 

In 1997, he was awarded a Miller Fellowship at Berkeley. Subsequently, he was a professor at MIT in the Mathematics Department, until he moved to Georgia Tech in 2006. 

His main work has been in the area of theoretical computer science, with particular activity in the fields of algorithms, randomized algorithms, computational geometry, and computational learning theory, including the authorship of books on random projection and spectral methods. 

In 2008, he co-founded the Computing for Good (C4G) program at Georgia Tech.

Vempala has received numerous awards, including a Guggenheim Fellowship, Sloan Fellowship, and being listed in Georgia Trend's 40 under 40.[5] He was named Fellow of ACM "For contributions to algorithms for convex sets and probability distributions" in 2015.[6] He was named a Fellow of the American Mathematical Society, in the 2022 class of fellows, "for contributions to randomized algorithms, high-dimensional geometry, and numerical linear algebra, and service to the profession".

Joel Sokol is the Harold E. Smalley Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He is also Director of the interdisciplinary Master of Science in Analytics degree (on-campus and online).

His primary research interests are in sports analytics and applied operations research. He has worked with teams or leagues in all three of the major American sports. Dr. Sokol's LRMC method for predictive modeling of the NCAA basketball tournament is an industry leader, and his non-sports research has won the EURO Management Science Strategic Innovation Prize and been a finalist for the Cozzarelli Prize.

Dr. Sokol has also won recognition for his teaching and curriculum development from IIE and the NAE, held the Fouts Family Associate Professorship for a three-year term, and is the recipient of Georgia Tech's highest awards for teaching. He served two terms as INFORMS Vice President of Education, and is a past Chair and founding officer of the INFORMS section on sports operations research.

Dr. Sokol's Ph.D. in operations research is from MIT, and his bachelor's degrees in mathematics, computer science, and applied sciences in engineering are from Rutgers University.