My research focuses on three major areas: (a) understanding and improving worker well-being, (b) temporal dynamics in team contexts, and (c) research methods. Collectively, my research seeks to improve our understanding of optimal human functioning more generally, across time, and within specific contexts (e.g., organizational, teams).

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).

King Jordan is Professor in the School of Biological Sciences and Director of the Bioinformatics Graduate Program at the Georgia Institute of Technology. He has a computational laboratory and his group works on a wide variety of research and development projects related to: (1) human clinical & population genomics, (2) computational genomics for public health, and (3) computational approaches to functional genomics. He is particularly interested in the relationship between human genetic ancestry and health. His lab is also actively engaged in capacity building efforts in genomics and bioinformatics in Latin America. 

Grover’s research activities in process systems engineering focus on understanding macromolecular organization and the emergence of biological function. Discrete atoms and molecules interact to form macromolecules and even larger mesoscale assemblies, ultimately yielding macroscopic structures and properties. A quantitative relationship between the nanoscale discrete interactions and the macroscale properties is required to design, optimize, and control such systems; yet in many applications, predictive models do not exist or are computationally intractable.

The Grover group is dedicated to the development of tractable and practical approaches for the engineering of macroscale behavior via explicit consideration of molecular and atomic scale interactions. We focus on applications involving the kinetics of self-assembly, specifically those in which methods from non-equilibrium statistical mechanics do not provide closed form solutions. General approaches employed include stochastic modeling, model reduction, machine learning, experimental design, robust parameter design, and estimation.

Dr. Omar I. Asensio is an Assistant Professor in the School of Public Policy at the Georgia Institute of Technology. His research focuses on the intersection of big data and public policy, with applications to energy systems and consumer behavior, smart cities, and machine learning in transportation and electric mobility. He directs the Data Science and Policy Lab at Georgia Tech, where he collaborates with the private sector and city governments on data innovations in policy analysis and research evaluation. He is a faculty affiliate at the Institute for Data Engineering and Science (IDEaS), the Machine Learning Center, and the Strategic Energy Institute. Dr. Asensio’s research has been published in leading journals such as Nature Energy, Nature Sustainability, and PNAS. His work uses statistical and computational tools to advance our understanding of how large-scale civic data and experiments can be used to increase participation in civic processes, while addressing resource conservation and environmental sustainability. Dr. Asensio’s research also has been featured in policy advisory communications by the European Commission, NSF Public Affairs, the World Bank, and national governments — including the U.K., and the IndiaAI initiative.

Dr. Asensio is a member of the New Voices 2021-2023 cohort of the National Academies of Sciences, Engineering and Medicine. He is a recipient of the National Science Foundation CAREER award, the Association for Public Policy Analysis and Management (APPAM) 40-for-40 fellowship, and the ONE-NBS Research Impact on Practice award by the Organizations and the Natural Environment (ONE) Division of the Academy of Management. Dr. Asensio serves as Associate Editor of Data and Policy journal published by Cambridge University Press. He holds a doctorate in environmental science and engineering from UCLA with field specialties in economics. He is a faculty participant in the Research University Alliance (RUA) Research Exchange and is engaged in multiple activities to increase the representation of women and under-represented students and professionals in STEM fields. 

Tom Conte holds a joint appointment in the Schools of Electrical & Computer Engineering and Computer Science at the Georgia Institute of Technology. He is the founding director of the Center for Research into Novel Computing Hierarchies (CRNCH). His research is in the areas of computer architecture and compiler optimization, with emphasis on manycore architectures, microprocessor architectures, back-end compiler code generation, architectural performance evaluation and embedded computer system architectures.

Edmond Chow is a Professor in the School of Computational Science in the College of Computing. He previously held positions at D. E. Shaw Research and Lawrence Livermore National Laboratory. His research is in developing and applying numerical methods and high-performance computing to solve large-scale scientific computing problems and seeks to enable scientists and engineers to solve larger problems more efficiently using physical simulation. Specific interests include numerical linear algebra (preconditioning, multilevel methods, sparse matrix computations) and parallel methods for quantum chemistry, molecular dynamics, and Brownian/Stokesian dynamics.  Chow earned an Honors B.A.Sc. in systems design engineering from the University of Waterloo, Canada, in 1993, and a Ph.D. in computer science with a minor in aerospace engineering from the University of Minnesota in 1997. Chow was awarded the 2009 ACM Gordon Bell prize and the 2002 Presidential Early Career Award for Scientists and Engineers (PECASE).