DR SEAN HON

After finishing his DPhil in mathematics at the University of Oxford, Sean is now an Assistant Professor at Hong Kong Baptist University. He has a keen interest in numerical analysis, computational finance, and mathematics of data science.

Sean's current research focuses on developing theory and numerical methods for evolutionary partial differential equations, option pricing, and deep learning.

He is also interested in efficient solvers for interfacial motions and preconditioning on Toeplitz-related systems. These research areas play crucial roles in scientific computation. For example, accurate numerical methods are needed for modelling multi-phase flow and spiral crystal growth, and image segmentation. Since the exact solutions of these problems are often unavailable, it is of great importance to develop efficient yet flexible methods which provide numerical approximations. As for Toeplitz-related systems, they are ubiquitous in maths and physics: they arise in numerical partial differential equations, approximation theory, compressed sensing, image processing, to name just a few. Thus it is essential to develop preconditioners to speed up the matrix inversion that involves such Toeplitz structure.