Shixin Xu is an applied mathematician working at the intersection of complex fluids, multiscale transport, and interpretable machine learning for healthcare and industry. His scholarship develops thermodynamically consistent models (Energy Variational Approach; phase-field/semipermeable membranes), structure-preserving numerics (energy-stable/compatible schemes; homogenization), and data-driven methods (NCART, class-balanced GBDT, PINNs with RSmote). He has served as PI or key contributor on NSFC General Program grants and multiple hospital–industry collaborations.

At Duke Kunshan, his teaching interests include calculus and advanced calculus; linear algebra for data science; numerical analysis, PDEs, and scientific computing; and mathematical/biological system modeling. His courses emphasize active learning, research-linked projects, and inclusive, real-world applications that connect mathematical theory to healthcare and engineering—fully aligned with DKU’s interdisciplinary liberal-arts mission.

He is the founder and convener of the DNAS Lunch Study Group; organizer/co-organizer of the Suzhou Area Junior Mathematicians Annual Workshop, the DKU Soft Matter Symposium, and the “Mathematics in Action” series; and a reviewer for SIAM journals, the Journal of Computational Physics, and PLOS Computational Biology.

Dr. Xu received his Ph.D. from the University of Science and Technology of China (USTC), followed by postdoctoral appointments at the National University of Singapore, the University of Notre Dame, the University of California, Riverside, and the Fields Institute (Toronto).