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. 

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.

Regents' Professor and Carter N. Paden, Jr. Distinguished Chair in Metals Processing, Dave McDowell joined Georgia Tech in 1983 and holds a dual appointment in the GWW School of Mechanical Engineering and the School of Materials Science and Engineering. He served as Director of the Mechanical Properties Research Laboratory from 1992-2012. In 2012 he was named Founding Director of the Institute for Materials (IMaT), one of Georgia Tech's Interdisciplinary Research Institutes charged with fostering an innovation ecosystem for research and education. He has served as Executive Director of IMaT since 2013. McDowell's research focuses on nonlinear constitutive models for engineering materials, including cellular metallic materials, nonlinear and time dependent fracture mechanics, finite strain inelasticity and defect field mechanics, distributed damage evolution, constitutive relations and microstructure-sensitive computational approaches to deformation and damage of heterogeneous alloys, combined computational and experimental strategies for modeling high cycle fatigue in advanced engineering alloys, atomistic simulations of dislocation nucleation and mediation at grain boundaries, multiscale computational mechanics of materials ranging from atomistics to continuum, and systems-based computational materials design. A Fellow of SES, ASM International, ASME and AAM, McDowell is the recipient of the 1997 ASME Materials Division Nadai Award for career achievement and the 2008 Khan International Medal for lifelong contributions to the field of metal plasticity. McDowell currently serves on the editorial boards of several journals, and is co-Editor of the International Journal of Fatigue.

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).

I am an Associate Professor in the School of Interactive Computing at Georgia Tech. I received my Ph.D. and B.S. degrees in Computer Science from Carnegie Mellon University, and held positions as a Postdoctoral Associate at the MIT Media Lab and as Assistant Professor at Worcester Polytechnic Institute prior to joining Georgia Tech. I direct the Robot Autonomy and Interactive Learning (RAIL) lab, where we work on developing robots that are able to effectively operate in human environments. My research interests span robotics and artificial intelligence, including semantic reasoning, adjustable autonomy, human computation and cloud robotics. Please visit the RAIL lab website for a description of our latest projects.