Zheng Chen, PhD
Liberal Arts 394E
- Numerical algorithms
- Data Science BS, BS/MS
- Data Science Graduate Certificate
- Data Science MS
- Engineering and Applied Science PhD
- Mathematics BA, BS
Course on numerical methods in science and engineering. Topics will include: numerical analysis and methods (quadrature, optimization, matrices, root-finding, ODEs, PDES, Monte-Carlo), and an introduction to multigrid and parallel computing. Programming exercises using MATLAB and individual research projects are an essential part of the course.
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.
A special course to meet the needs of students for material not encountered in other courses. Topics dealt with require the approval of the departmental chairperson.
- Numerical analysis, scientific computing, high performance computing
- Machine learning, image processing, neural networks
- Kinetic problems, multi-scale computational methods
- Numerical methods for problems with singularities
- Uncertainty quantification, fractional-order partial differential equations