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faculty
Bo Dong, PhD
Associate 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.
A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.
Written presentation of an original research topic in Data Science which demonstrates the knowledge & capability to conduct independent research. The thesis shall be completed under the supervision of a faculty advisor. An oral examination in defense is required.
Written presentation of an original research topic in Data Science which demonstrates the knowledge & capability to conduct independent research. The thesis shall be completed under the supervision of a faculty advisor. An oral examination in defense is required.
Investigations of a fundamental and/or applied nature representing an original contribution to the scholarly research literature of the field. PhD dissertations are often published in refereed journals or presented at major conferences. A written dissertation must be completed in accordance with the rules of the Graduate School and the College of Engineering. Admission to the course is based on successful completion of the PhD comprehensive examination and submission of a formal proposal endorsed by the student's graduate committee and submitted to the EAS Graduate Program Director.
An introduction to ordinary differential equations and their analysis. Topics cover first order linear and nonlinear ordinary differential equations, second order and higher order homogeneous and nonhomogeneous linear differential equations, the linear system of ordinary differential equations, qualitative analysis, numerical solutions, series solutions.
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.
Study under the supervision of a faculty member in an area covered in a regular course not currently being offered. Conditions and hours to be arranged.
Research
Research interests
- Numerical analysis and scientific computing
- Finite element methods, discontinuous Galerkin methods