Kailiang Wu

Kailiang Wu is an Associate Professor at the Department of Mathematics, Southern University of Science and Technology (SUSTech), and holds concurrent roles at the Shenzhen International Center for Mathematics and National Center for Applied Mathematics Shenzhen. His research bridges Machine Learning and Computational Fluid Dynamics, focusing on High-Order Numerical Methods for Hyperbolic Conservation Laws and Relativistic Astrophysics.

• Education: Ph.D. in Mathematics (Peking University, 2016), B.Sc. in Mathematics and Statistics (Huazhong University of Science and Technology, 2011)

His work develops Structure-Preserving Schemes for multidimensional PDEs, including Oscillation-Eliminating Discontinuous Galerkin (OEDG) and Geometric Quasilinearization (GQL) frameworks. These methods ensure positivity, divergence-free, and bound-preservation in simulations of relativistic flows and MHD systems.

Recent publications emphasize Deep Learning applications in operator learning (e.g., DUE framework) and Data-Driven Modeling of unknown PDEs. His group has produced 20+ peer-reviewed articles in top journals (Math. Comp., SIAM J. Numer. Anal., JCP) since 2014.

• Honors:
  • SUSTech President's Research Award (2025)
  • World's Top 2% Scientist (2024)
  • NSFC Major Program (2023, 0.7M CNY)
  • Shenzhen Distinguished Young Scholar (2023, 4M CNY)
  • National Excellent Young Scholar Program (2020, 2M CNY)
  • Zhong Jiaqing Mathematics Award (2019)

He advises 10+ graduate students and postdocs, with alumni securing academic positions at Sun Yat-sen University and HKUST. His lab collaborates on relativistic hydrodynamics, traffic models, and uncertainty quantification, supported by competitive funding.