Qi Tang is an assistant professor in the School of Computational Science and Engineering at the Georgia Institute of Technology. Before joining Georgia Tech, he was a staff scientist in the Applied Mathematics and Plasma Physics Group in the Theoretical Division at Los Alamos National Laboratory. His research develops structure-preserving numerical methods and machine learning for multiscale, multiphysics systems, with a focus on plasma dynamics and fusion energy science. His interests include magnetohydrodynamics, kinetic modeling and kinetic-to-fluid closures, scalable numerical algorithms for extreme-scale simulation, and scientific machine learning that encodes conservation laws, symmetries, and geometric structure. Across these areas, his work bridges first-principles models and data-driven methods to enable predictive simulation of plasma systems in which classical scale-separation and near-equilibrium assumptions break down. He is a recipient of the DOE Early Career Award.