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faculty
Bo Dong, PhD
Professor
Mathematics
Contact
508-910-6616
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Spruce Hall 0174
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.
Doctoral thesis proposal development based on technical writing process, data interpretation, experimental design. Students who successfully complete the course will be able to assess information from the primary scientific literature, formulate scientific questions (hypotheses), and generate an experimental plan to help validate or nullify their hypothesis. Students will demonstrate a command of oral and written communication skills by completing this course.
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