Teaching/Students

Teaching

In Fall 2026, I teach the following courses:

• M105 – Contemporary Mathematics

• M601 – Real Analysis I

Recent graduate topics courses include M591 – Computational Partial Differential Equations and Data (Fall 2025) and M591 – Data-driven Deep Learning and Partial Differential Equations (Spring 2026). These courses focus on partial differential equations, applied analysis, and numerical methods for PDEs, with an emphasis on connecting rigorous mathematical theory with computation and applications.

Across both undergraduate and graduate courses, my goal is to help students develop a solid analytical foundation while gaining experience with modern computational tools and learning how theoretical insight informs effective numerical modeling.

Students and Mentoring

I have supervised and co-supervised graduate students working on topics in singular perturbation problems, boundary layer analysis, numerical methods for stiff PDEs, and hybrid analytical–computational approaches.

Recent doctoral mentoring includes Teng-Yuan Chang (Ph.D. in Applied Mathematics, National Yang Ming Chiao Tung University, 2024; co-advised with Ming-Cheng Shiue), who continues as a collaborator on singular-layer PINN methods for Burgers’ equation, plane-parallel flows, and the Navier–Stokes equations.

I welcome motivated undergraduate and graduate students, as well as postdoctoral researchers, interested in applied analysis, numerical methods, and multi-scale problems in partial differential equations, particularly those who wish to combine rigorous mathematical analysis with computation.