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).

Neu's research involves the understanding and prediction of the fatigue behavior of materials and closely related topics, typically when the material must resist degradation and failure in harsh environments. Specifically, he has published in areas involving thermomechanical fatigue, fretting fatigue, creep and environmental effects, viscoplastic deformation and damage development, and related constitutive and finite-element modeling with a particular emphasis on the role of the materials microstructure on the physical deformation and degradation processes. He has investigated a broad range of structural materials including steels, titanium alloys, nickel-base superalloys, metal matrix composites, molybdenum alloys, high entropy alloys, medical device materials, and solder alloys used in electronic packaging. His research has widespread applications in aerospace, surface transportation, power generation, machinery components, medical devices, and electronic packaging. His work involves the prediction of the long-term reliability of components operating in extreme environments such as the hot section of a gas turbine system for propulsion or energy generation. His research is funded by some of these industries as well as government funding agencies.

Dr. Christopher Saldaña began working at Georgia Tech in 2014. Prior, Dr. Saldaña previously held the Harold and Inge Marcus Career Professorship at the Pennsylvania State University and worked as a research engineer at M4 Sciences Corporation. Dr. Saldaña has also previously held visiting affiliations/positions with the US Air Force Research Laboratory, the Indian Institute of Science (Bangalore, India), Technische Universität Dortmund (Dortmund, Germany), Autodesk, and Sandia National Laboratories. He has received several awards, including an NSF CAREER award, the Robert J. Hocken SME Outstanding Young Manufacturing Engineer award and an R&D100 Technology Award. He serves as an Associate Editor for IISE Transactions (Design and Manufacturing) and serves on the Editorial Boards of Manufacturing Letters, Computer Aided Design and Applications, and the ASTM Journal of Smart and Sustainable Manufacturing.

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).

Saman Zonouz is an Associate Professor at the Schools of Cybersecurity and Privacy (SCP), and Electrical and Computer Engineering (ECE) at Georgia Tech. Previously, he was a faculty at Rutgers University. His research focuses on security and privacy research problems in cyber-physical systems including the attack detection and response capabilities using techniques from systems security and control theory. Saman's research has been awarded by Presidential Early Career Awards for Scientists and Engineers (PECASE), NSF CAREER Award in Cyber-Physical Systems (CPS), Significant Research in Cyber Security by the National Security Agency (NSA), and Faculty Fellowship Award by Air Force Office of Scientific Research (AFOSR). Saman obtained his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2011. 

Pascal Van Hentenryck is an A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Prior to this appointment, he was a professor of Computer Science at Brown University for about 20 years, he led the optimization research group (about 70 people) at National ICT Australia (NICTA) (until its merger with CSIRO), and was the Seth Bonder Collegiate Professor of Engineering at the University of Michigan. Van Hentenryck is also an Honorary Professor at the Australian National University.

Van Hentenryck is a Fellow of AAAI (the Association for the Advancement of Artificial Intelligence) and INFORMS (the Institute for Operations Research and Management Science). He has been awarded two honorary doctoral degrees from the University of Louvain and the university of Nantes, the IFORS Distinguished Lecturer Award, the Philip J. Bray Award for teaching excellence in the physical sciences at Brown University, the ACP Award for Research Excellence in Constraint Programming, the ICS INFORMS Prize for Research Excellence at the Intersection of Computer Science and Operations Research, and an NSF National Young Investigator Award. He received a Test of Time Award (20 years) from the Association of Logic Programming and numerous best paper awards, including at IJCAI and AAAI. Van Hentenryck has given plenary/semi-plenary talks at the International Joint Conference on Artificial Intelligence (twice), the International Symposium on Mathematical Programming, the SIAM Optimization Conference, the Annual INFORMS Conference, NIPS, and many other conferences. Van Hentenryck is program co-chair of the AAAI’19 conference, a premier conference in Artificial Intelligence.

Van Hentenryck’s research focuses in Artificial Intelligence, Data Science, and Operations Research. His current focus is to develop methodologies, algorithms, and systems for addressing challenging problems in mobility, energy systems, resilience, and privacy. In the past, his research focused on optimization and the design and implementation of innovative optimization systems, including the CHIP programming system (a Cosytec product), the foundation of all modern constraint programming systems and the optimization programming language OPL (now an IBM Product). Van Hentenryck has also worked on computational biology, numerical analysis, and programming languages, publishing in premier journals in these areas.

Van Hentenryck runs the Seth Bonder summer Camp in Computational and Data Science for middle- and high-school students every summer. 

Dr. Glezer began at Tech in 1992 as an Associate Professor. He was named to the Woodruff Chair in Thermal Systems in 2002. Prior, he was an Assistant and Associate Professor at the University of Arizona.

Teaching Interests

Professor Glezer's teaching interests encompass core mechanical engineering subjects at both undergraduate and graduate levels, including fluid mechanics, aerodynamics, heat transfer, and energy systems. His courses emphasize fundamental principles, analytical methods, and experimental techniques. He fosters an interactive learning environment and actively engages graduate and undergraduate students in research-driven projects to deepen their understanding of fluid dynamics and energy conversion.

Research Interests

Professor Glezer's research focuses on fluid mechanics and aerodynamics with an emphasis on flow control, fluid-structure interactions, and energy transport phenomena. His work explores the fundamental physics of turbulence, vortex dynamics, and aerodynamic performance enhancement, employing experimental and computational approaches to address challenges in aerodynamics, propulsion systems, energy systems, and thermal management.

Recent Publications

• Nichols, D. A., Vukasinovic, B., and Glezer, A., “On the Formation of a Ground Vortex In Crossflow,” J. Fluid Mech., 1012, 2025. 
• Peterson, C. J., Vukasinovic, B., and Glezer, A., “The Evolution and Scaling of Controlled Flow Separation at the Inlet of a Diverging Channel,” Exp. Fluids, 66, 2025. 
• Burrows, T.J., Vukasinovic, B., Glezer, A., Lakebrink, M.T., and Mani, M., “Controlled Flow in a Serpentine Diffuser with a Cowl Inlet,” J. Aircraft, 62, 2025.
• Vukasinovic, B., Funk, R. B., and Glezer, A., “Circulation Control using Arrays of Fluidically Oscillating Jets,” AIAA J., 63, 2025.
• Boziuk, T. R., Smith, M. K., and Glezer, A., “Dynamics of Vapor Bubble Condensation under Directional Ultrasonic Actuation,” Phys. Fluids, 35, 2023.

Aarn Stebner works at the intersection of manufacturing, machine learning, materials, and mechanics. He joined the Georgia Tech faculty as an associate professor of Mechanical Engineering and Materials Science and Engineering in 2020.

Previously, he was the Rowlinson Associate Professor of Mechanical Engineering and Materials Science at the Colorado School of Mines (2013 – 2020), a postdoctoral scholar at the Graduate Aerospace Laboratories of the California Institute of Technology (2012 – 2013), a Lecturer in the Segal Design Institute at Northwestern University (2009 – 2012), a Research Scientist at Telezygology Inc. establishing manufacturing and “internet of things” technologies for shape memory alloy-secured latching devices (2008-2009), a Research Fellow at the NASA Glenn Research Center developing smart materials technologies for morphing aircraft structures (2006 – 2008), and a Mechanical Engineer at the Electric Device Corporation in Canfield, OH developing manufacturing and automation technologies for the circuit breaker industry (1995 – 2000).

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).

Wang's research is in the areas of design, manufacturing, and Integrated computational materials engineering. He is interested in computer-aided design, geometric modeling and processing, computer-aided manufacturing, multiscale simulation, and uncertainty quantification.

Currently, Wang studies integrated product-materials design and manufacturing process design, where process-structure-property relationships are established with physics-based data-driven approaches for design optimization. The Multiscale Systems Engineering research group led by him develops new methodologies and computational schemes to solve the technical challenges of high dimensionality, high complexity, and uncertainty associated with product, process, and systems design at multiple length and time scales.

Computational design tools for multiscale systems with sizes ranging from nanometers to kilometers will be indispensable for engineers' daily work in the near future. The research mission of the Multiscale Systems Engineering group is to create new modeling and simulation mechanisms and tools with underlying scientific rigor that are suitable for multiscale systems engineering for better and faster product innovation. Our education mission is to train engineers of the future to gain necessary knowledge as well as analytical, computational, communication, and self-learning skills for future work in a collaborative environment as knowledge creators and integrators.