Summary
Xueqin Huang is a research scientist and PhD candidate in Computational Materials Science with 11 years of experience applying machine learning and physics-based modeling to materials problems. She combines computer vision for defect detection in metallurgical microscopy with implementation of fluid-dynamics and dissipation phase-field models to predict microstructure in additive manufacturing, often leveraging GPUs for large-scale simulations. Her industry experience spans Meta and Arm, where she translated research into production-focused ML features and hardware-aware inference techniques. Based in Shanghai and trained at Texas A&M and SUSTech, she bridges deep physics intuition with practical software and ML engineering—an uncommon mix that accelerates moving models from lab-scale simulations toward deployable tooling.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Materials Science, Doctor of Philosophy - PhD, Materials Science at Texas A&M University
Bachelor's degree, Physics, 3.8/4.0, Bachelor's degree, Physics, 3.8/4.0 at Southern University of Science and Technology