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.

Valerie Thomas is the Anderson-Interface Chair of Natural Systems and Professor in the H. Milton School of Industrial and Systems Engineering, with a joint appointment in the School of Public Policy. 

Dr. Thomas's research interests are energy and materials efficiency, sustainability, industrial ecology, technology assessment, international security, and science and technology policy. Current research projects include low carbon transportation fuels, carbon capture, building construction, and electricity system development. Dr. Thomas is a Fellow of the American Association for the Advancement of Science, and of the American Physical Society. She has been an American Physical Society Congressional Science Fellow, a Member of the U.S. EPA Science Advisory Board, and a Member of the USDA/DOE Biomass Research and Development Technical Advisory Committee. 

She has worked at Princeton University in the Princeton Environmental Institute and in the Center for Energy and Environmental Studies, and at Carnegie Mellon University in the Department of Engineering and Public Policy.

Dr. Thomas received a B. A. in physics from Swarthmore College and a Ph.D. in theoretical physics from Cornell University.

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.

George Lan is an A. Russell Chandler III Professor of Industrial and Systems Engineering at Georgia Institute of Technology.  His research and teaching interests lie in theory, algorithms and applications of stochastic optimization and nonlinear programming.  Most of his current research concerns the design of efficient algorithms for solving challenging optimization problems, especially those arising from data analytics, machine learning, and reinforcement learning. He is actively pursuing the applications of these methodologies in healthcare and sustainability areas. Dr. Lan serves as the associate editor for Computational Optimization and Applications (2014 – present), Mathematical Programming (2016 – present) and SIAM Journal on Optimization (2016  – present). Dr. Lan is an associate director for the center of machine learning at Georgia Tech.

Lily Cheung got her research start as a sophomore at Rutgers University, where she graduated Summa Cum Laude with a B.S. in Chemical Engineering in 2008. She then earned her Ph.D. in Chemical Engineering from Princeton University in 2013. Under the supervision of Stanislav Shvartsman, she characterized gene regulatory networks controlling the development of the model organism Drosophila melanogaster, using a combination of molecular biology, genetics, and reaction-diffusion modeling.

During her postdoctoral training with Wolf Frommer at the Carnegie Institution for Science, she designed biomolecular sensors to quantify sugar transport in plants. Her current interests include the use of high-throughput quantitative techniques and mathematical modeling to advance our understanding of how metabolic and gene regulatory networks interact to control plant growth.

Lily is the recipient of a NSF NPGI Postdoctoral Fellowship in Biology, a NSF CAREER Award, and a Human Frontier Science Program Early Career Award.

The Tong Lab tackles challenges in the interdisciplinary areas of bioresource engineering and sustainable chemistry. We develop innovative technologies for producing chemicals, materials, energy, and fuels from renewable resources.

Current research interests include:

• Functional biomaterials for high-efficiency circular economy
• Platform chemicals and hydrocarbon fuels from renewable resources
• Sustainable process control and modeling
• Nano-biomaterial synthesis and self-assembling
• Polymer degradation and recycling

Disciplines:

• Materials and Nanotechnology

• Energy and Sustainability

Dr. Akanksha Menon is an Assistant Professor in the Woodruff School of Mechanical Engineering at Georgia Tech. Prior to this, she was a Rosenfeld Postdoctoral Fellow at Lawrence Berkeley National Laboratory, where she performed research on hybrid membrane-thermal desalination processes using solar energy, and she also contributed to the development of thermal energy storage materials. Dr. Menon completed her Ph.D. at Georgia Tech, where she focused on developing semiconducting polymers and new device architectures for thermoelectric energy harvesting. She holds a bachelor's degree from Texas A&M University at Qatar, as well as a master’s degree in Mechanical Engineering from Georgia Tech.

Her research group at Georgia Tech is working on technologies for the water-energy nexus.