Bo Dong

faculty

Bo Dong, PhD

Associate Professor

Mathematics

Research Website

508-910-6616

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Liberal Arts 394D

Education

2007University of MinnesotaPhD
2002University of Science and Technology of ChinaBS

Teaching

  • Differential Equations
  • Numerical Analysis
  • Calculus, Linear Algebra

Teaching

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.

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.

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.

Introduction to the diverse ethical concerns, challenges and responsibilities that arise when engaging in scientific research. Students will have opportunities to reflect upon and discuss their own ethical constructs in the face of practical ethical dilemmas.

A seminar series on interdisciplinary research topics by prominent speakers in EAS fields and student presentations on research in progress. May be repeated for credit.

Prerequisite: Graduate standing; approval by advisor, graduate program director and department chairperson. Experiential learning in conjunction with an industrial or governmental agency project under the joint supervision of an outside sponsor and a faculty advisor. To be eligible, a student should have completed at least half of his/her program of study. A detailed project proposal must be prepared by the student for departmental approval prior to the start of the project. Upon completion, student must submit a report on the experience and make a short presentation to his/her graduate committee. This course may be used to satisfy one 3-credit graduate technical elective course.

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.

Research

Research interests

  • Numerical analysis and scientific computing
  • Finite element methods, discontinuous Galerkin methods