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faculty
Bo Dong, PhD
Professor
Mathematics
Contact
508-910-6616
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Liberal Arts 394D
Education
| 2007 | University of Minnesota | PhD |
| 2002 | University of Science and Technology of China | BS |
Teaching
- Differential Equations
- Numerical Analysis
- Calculus, Linear Algebra
Teaching
Programs
Programs
Teaching
Courses
Matrix methods with emphasis on applied data analysis. Matrix norms; LU, QR and SV decomposition of matrices; least squares problems, orthogonal vectors and matrices; applications to data analysis.
Research investigations of a fundamental and/or applied nature defining a topic area and preliminary results for the dissertation proposal undertaken before the student has qualified for EAS 701. With approval of the student's graduate committee, up to 15 credits of EAS 601 may be applied to the 30 credit requirement for dissertation research.
Research investigations of a fundamental and/or applied nature defining a topic area and preliminary results for the dissertation proposal undertaken before the student has qualified for EAS 701. With approval of the student's graduate committee, up to 15 credits of EAS 601 may be applied to the 30 credit requirement for dissertation research.
A calculus-based introduction to statistics. This course covers probability and combinatorial problems, discrete and continuous random variables and various distributions including the binomial, Poisson, hypergeometric normal, gamma and chi-square. Moment generating functions, transformation and sampling distributions are studied.
Orthogonality and least square problems. Other topics include applications of eigenvalue, quadratic forms, Numerical Linear Algebra.
Numerical methods for solving initial value problems. Topics include: numerical differentiation and integration, Euler method and Taylor's series method, Runge-Kutta methods, multi-step methods, and stiff equations
An introduction to numerical linear algebra. Numerical linear algebra is fundamental to all areas of computational mathematics. This course will cover direct numerical methods for solving linear systems and linear least squares problems, stability and conditioning, computational methods for finding eigenvalues and eigenvectors, and iterative methods for both linear systems and eigenvalue problems.
An introduction to numerical linear algebra. Numerical linear algebra is fundamental to all areas of computational mathematics. This course will cover direct numerical methods for solving linear systems and linear least squares problems, stability and conditioning, computational methods for finding eigenvalues and eigenvectors, and iterative methods for both linear systems and eigenvalue problems.
Research
Research interests
- Numerical analysis and scientific computing
- Finite element methods, discontinuous Galerkin methods