Bin Han, PhD

Area of Study / Keywords

Applied Mathematics Applied Harmonic Analysis Scientific Computing Approximation Theory

About

My research area is on wavelet theory and its applications to image processing, data sciences, CAGD, and scientific computing. Published the book: Framelets and Wavelets: Algorithms, Analysis and Applications, (2017). Personal web page is at http://www.ualberta.ca/~bhan

Research

Areas

Applied and Computational Harmonic Analysis, Wavelets and Framelets, Scientific Computing and Numerical Algorithms, Wavelet-based Image Processing and Data Sciences, Splines and Approximation Theory

MATH 535 - Numerical Methods I

Direct and iterative methods for solving linear systems, iterative methods for nonlinear systems, polynomial and spline interpolations, least square approximation, numerical differentiation and integration, initial value problems for ODE's (one-step, multistep methods, stiff ODE's). Prerequisite: 400-level MATH course. Students are required to have knowledge of advanced Calculus and introductory knowledge in Analysis and Linear Algebra and some computer programming. Note 1: Restricted to graduate students only. Note 2: May not be taken for credit if credit has already been obtained in MATH 381, 481 or 486 or equivalent.

MATH 536 - Numerical Solutions of Partial Differential Equations I

Finite difference and finite element methods for boundary-value problems of elliptic equations. Numerical algorithms for large systems of linear algebraic equations: direct, classical relaxation, multigrid and preconditioned conjugate gradient methods. Algorithms for vector/parallel computers and the domain decomposition method. Prerequisites: MATH 337, 436 or equivalent and some computer programming.

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