Shukai Du

Shukai Du is an Assistant Professor of Mathematics at Syracuse University, within the College of Arts and Sciences. Previously, he served as a Visiting Assistant Professor at the University of Wisconsin-Madison (2020–2024) and earned his Ph.D. in Applied Mathematics from the University of Delaware (2020). His research focuses on numerical analysis, scientific machine learning, and inverse problems, with expertise in discontinuous Galerkin methods, radiative transfer, and adaptive mesh refinement. He is also affiliated with his research website.

Education:

• Ph.D., Applied Mathematics, University of Delaware (2020)
• M.S., Computational Mathematics, Wuhan University (2015)
• B.S., Pure Mathematics, Wuhan University (2012)

Research Highlights:

Dr. Du’s work emphasizes accelerating numerical methods via machine learning (element learning), developing goal-oriented hp-adaptive mesh refinement for inverse radiative transfer, and creating symplectic Hamiltonian finite element methods for electromagnetics. His methods prioritize accuracy, computational efficiency, and preservation of physical invariants.

Recent Contributions:

Recent publications address operator learning for finite element acceleration, adaptive-mesh inversion for inverse problems, and energy-conserving discretizations for Hamiltonian systems. His work bridges classical numerical methods and modern data-driven techniques.

Awards:

• Wenbo Li Prize (2020, University of Delaware)

Grants:

• "Breaking the 1D Barrier in Radiative Transfer: Fast, Low-Memory Numerical Methods for Enabling Inverse Problems and Machine Learning Emulators" (NSF, 2023–2026)
• "Convective Processes in the Tropics Across Scales" (NSF, 2024–2026)

Labs/Teams:

His research collaborates with computational scientists and applied mathematicians, focusing on interdisciplinary challenges in computational physics and data-driven modeling.