Machine Learning Accelerated and First Principles Based Multiscale Simulations for Renewable Energy and Biomedicine
Sijia Dong is an assistant professor in the Department of Chemistry and Chemical Biology at Northeastern University. Sijia is passionate about accelerating science using computation and automation. She received her Ph.D. in Chemistry from the California Institute of Technology in 2017, advised by Prof. William A. Goddard III, with whom and Dr. Ravinder Abrol she developed a first-principles-based and data-driven computational method to predict the structures of proteins that are crucial drug targets for many diseases. She carried out her postdoctoral research at the University of Minnesota with Prof. Donald G. Truhlar and Prof. Laura Gagliardi, and then at Argonne National Laboratory with Prof. Giulia Galli. Her postdoctoral work was to use and develop quantum chemical methods and workflows to study the photochemistry of molecules and materials in light-harvesting systems and to use machine learning to accelerate quantum chemical methods. Research in the Dong Lab focuses on developing and applying physics-based and data-driven computational methods to understand multiscale processes, from electronic structures to emergent properties, and to design molecules, materials, and processes for renewable energy, biomedicine, and beyond.
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Meeting ID: 942 4035 1473