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).

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. 

Dr. Medford is interested in leveraging materials informatics, statistics, and machine learning to maximize the practical impact of fundamental atomic-scale simulations in the field of surface science and catalysis. His research areas include heterogeneous catalysis, oxide surface chemistry, density functional theory, kinetic models, uncertainty quantification, and Bayesian optimization and inference.

Victor Fung is an Assistant Professor in the School of Computational Science and Engineering. Prior to this position, he was a Wigner Fellow and a member of the Nanomaterials Theory Insitute in the Center for Nanophase Materials Sciences at Oak Ridge National Laboratory. A physical chemist by training, Fung now works at the intersection of scientific artificial intelligence, computing, and materials science/chemistry.

Lu Gan joined the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology as an Assistant Professor in January 2024. She leads the Lu's Navigation and Autonomous Robotics (Lunar) Lab at Georgia Tech, and is on the core faculty of the Institute for Robotics and Intelligent Machines. Her research interests include robot perception, robot learning, and autonomous navigation. Her group explores the use of computer vision, machine learning, estimation, probabilistic inference, kinematics and dynamics to develop autonomous systems in ground, air, and space applications.

She holds a B.S. in Automation from the University of Electronic Science and Technology of China, an M.S. in Control Engineering from Beihang University, and received her M.S. and Ph.D. in Robotics from the University of Michigan, Ann Arbor. Before joining Georgia Tech, she had a two-year appointment as a Postdoctoral Scholar at the Graduate Aerospace Laboratories of the California Institute of Technology and the Center for Autonomous Systems and Technologies at Caltech.

May Dongmei Wang, Ph.D., is The Wallace H Coulter Distinguished Faculty Fellow, professor of BME, ECE and CSE, Director of Biomedical Big Data Initiative, and Georgia Distinguished Cancer Scholar. She is also Petit Institute Faculty Fellow, Kavli Fellow, Fellow of AIMBE, Fellow of IEEE, and Fellow of IAMBE. She received BEng from Tsinghua University China and MS/PhD from Georgia Institute of Technology (GIT). Dr. Wang’s research and teaching are in Biomedical Big Data and AI-Driven Biomedical Health Informatics and Intelligent Reality (IR) for predictive, personalized, and precision health. She has published over 270 referred journal and conference proceeding articles (13,500+ GS-Citations) and delivered over 280 invited and keynote lectures. Dr. Wang’s research has been supported by NIH, NSF, CDC, GRA, GCC, VA, Children’s Healthcare of Atlanta, Enduring Heart Foundation, Wallace Coulter Foundation, Carol Ann and David Flanagan Foundation, Shriner’s Hospitals, Microsoft Research, HP, UCB, and Amazon.

Dr. Wang chairs IEEE Engineering in Medicine and Biology Society (EMBS) BHI-Technical Community and ACM Special Interest Group in Bioinformatics (SIGBio), and is the Senior Editor of IEEE Journal of Biomedical & Health Informatics (IF=7.02), and Associate Editor for IEEE Transactions on BME, and IEEE Review of BME. She was IEEE EMBS Distinguished Lecturer and PNAS (Proceeding of National Academy of Sciences) Emerging Area Editor. During the past decade, Dr. Wang has been a standing panelist for NIH Study Sections, NSF Smart and Connect Health, and Brain Canada, and has co-chaired and helped organize more than 10 conferences by IEEE Engineering in Medicine and Biologics  Gordon Research Conferences, ACM Special Interest Groups in Bioinformatics, and IEEE Future Directions.

Dr. Wang received GIT Outstanding Faculty Mentor for Undergrad Research Award and Emory University MilliPub Award for a high-impact paper cited over 1,000 times. She was selected into 2022 Georgia Tech LeadingWomen Program and 2021 Georgia Tech Provost Emerging Leaders Program. Previously, she was Carol Ann and David Flanagan Distinguished Faculty Fellow, GIT Biomedical Informatics Program Co-Director in ACTSI, and Bioinformatics and Biocomputing Core Director in NIH/NCI-Sponsored U54 Center for Cancer Nanotechnology Excellence.

Marilyn Brown is a Regents' and Brook Byers Professor of Sustainable Systems in the School of Public Policy. She joined Georgia Tech in 2006 after a distinguished career at the U.S. Department of Energy's Oak Ridge National Laboratory, where she led several national climate change mitigation studies and became a leader in the analysis and interpretation of energy futures in the United States. 

Her research focuses on the design and impact of policies aimed at accelerating the development and deployment of sustainable energy technologies, with an emphasis on the electric utility industry, the integration of energy efficiency, demand response, and solar resources, and ways of improving resiliency to disruptions. Her books include Fact and Fiction in Global Energy Policy (Johns Hopkins University Press, 2016), Green Savings: How Policies and Markets Drive Energy Efficiency (Praeger, 2015), and Climate Change and Global Energy Security (MIT Press, 2011). She has authored more than 250 publications. Her work has had significant visibility in the policy arena as evidenced by her numerous briefings and testimonies before state legislative bodies and Committees of both the U.S. House of Representatives and Senate.

Dr. Brown co-founded the Southeast Energy Efficiency Alliance and chaired its Board of Directors for several years. She has served on the Boards of the American Council for an Energy-Efficient Economy and the Alliance to Save Energy, and was a commissioner with the Bipartisan Policy Center. She has served on eight National Academies committees and is an Editor of Energy Policy and an Editorial Board member of Energy Efficiency and Energy Research and Social Science. She served two terms (2010-2017) as a Presidential appointee and regulator on the Board of Directors of the Tennessee Valley Authority, the nation’s largest public power provider. From 2014-2018 she served on DOE’s Electricity Advisory Committee, where she led the Smart Grid Subcommittee.

Debra Lam is the Founding Director of the Partnership for Inclusive Innovation, a statewide public-private partnership committed to investing in innovative solutions for shared economic prosperity. She continues to lead smart communities and urban innovation work at Georgia Tech. Prior to this, she served as Pittsburgh’s inaugural Chief of Innovation & Performance where she oversaw all technology, sustainability, performance, and innovation functions of city government. Before that, she was a management consultant at a global engineering and design firm, Arup. She has received various awards, including being named one of the top 100 most influential people in digital government by Apolitcal.

She has worked and lived in the United Kingdom, China, Taiwan, and Hong Kong. A graduate of Georgetown University and the University of California, Berkeley, Debra serves on the board of the Community Foundation of Greater Atlanta and was most recently appointed by the U.S Department of Commerce to the Internet of Things Advisory Board.

Calton Pu, Ph.D., is a Professor in the School of Computer Science, College of Computing at the Georgia Institute of Technology. His research interests are in the areas of distributed computing, Internet data management, and operating systems. His current projects fall under the areas of cloud computing (Elba) and big data (GRAIT-DM) research. Using experimental data from realistic benchmarks, the Elba project studies the  phenomena of very short bottlenecks that have large impact on N-tier system response time. The GRAIT-DM project collects real world data from social sensors (e.g., Twitter and YouTube) and physical sensors (e.g., USGS GSN and NASA TRMM) to detect physical events and manage real-time information on them. The sponsors for Pu's research include both government funding agencies such as the National Science Foundation, and companies from industry such as IBM, Intel, and Hewlett-Packard.  He is a co-director of Center for Experimental Research in Computer Systems (CERCS) and affiliate of the Institute for Information Security and Privacy (IISP) at Georgia Tech.  Pu has taught several courses in the areas of systems and databases. In Fall, he teaches CS4220/6235 Real-Time Embedded Systems. In spring, he teaches CS4365/6365 Introduction to Enterprise Computing.