Fani Boukouvala
programming and advanced data analytics for the development of novel modeling and optimization tools. Specificially, she looks at applications in energy and Boukouvala, who hails from Athens, Greece, was a postdoctoral pharmaceuticals. Co-author of 12 published researcher at Texas A&M University papers, she studies algorithmic and Princeton University prior to developments in data-driven joining Georgia Tech. modeling and optimization; bigAs the role of computer simulations grows in science, and as data approaches for optimization of energy efficiency; simulation-based advanced instrumentation enables optimization under uncertainty for the collection of large amounts of CO2 sequestration; and process data, Boukouvala is interested in modeling and optimization for drug the interface of traditional process design and manufacturing. systems engineering, mathematical Assistant Professor PhD, Rutgers University, 2016
Nian Liu
Assistant Professor (starting Jan. 2017) PhD, Stanford University, 2014 After obtaining his PhD from Stanford in 2014, Liu continued there as a postdoctoral research fellow. He designs nanomaterials for high-energy batteries and develops light microscopy to visualize battery reactions. Co-author of 60 published papers, Liu won the Young Investigator Award from the American Chemical Society’s
Division of Inorganic Chemistry in 2015 as well as the Electrochemical Society’s Daniel Cubicciotti Award in 2014. He was selected to present at the 2015 Rising Stars in Chemistry Symposium organized by the University of Chicago. Before starting his undergraduate studies in 2005 at Fudan University in Shanghai, China, Liu won a gold medal in the China National Chemistry Olympiad, among 150,000 competitors. In 2014, he won the Chinese Government Award for Outstanding Self-financed Students Abroad.
Andrew Medford
Assistant Professor (starting Jan. 2017) PhD, Stanford University, 2015 A postdoctoral researcher in Georgia Tech’s MINED (Materials Informatics for Engineering Design) Group, 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. Co-author of more than two dozen published papers, Medford won an Outstanding Teaching Assistant Award at Stanford, and held a National Defense Science and Engineering Graduate Research Fellowship. He recently participated in the grand-prize-winning project of the Air Force Research Laboratory’s Materials Science and Engineering Data Challenge.
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