/filters:quality(90)/prod01/production-cdn-pxl/media/umassdartmouth/profiles/arts-and-sciences/mathematics/Zheng-Chen.jpg?text=945+WebP)
faculty
Zheng Chen, PhD
Associate Professor
Mathematics
Contact
508-999-9236
xafcl0>sk_qqb,cbs
Liberal Arts 394C
Education
| 2014 | Brown University | PhD |
| 2010 | Brown University | MS |
Teaching
- Numerical algorithms
- Calculus
Teaching
Programs
Programs
- Data Science BS, BS/MS
- Data Science Graduate Certificate
- Data Science MS
- Engineering and Applied Science PhD
- Mathematics BA, BS
Teaching
Courses
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.
An intensive study of advanced algebra and trigonometry. Topics include: linear, quadratic, polynomial, rational, exponential, logarithmic and trigonometric functions, modeling and graphing these functions, and the effects of affine transformations on the graphs of functions. This course prepares students for the study of Calculus I (MTH 151 or MTH 153), which is required for majors in Mathematics, Physics, Chemistry, Engineering and Mathematical/Computational Biology. This course fulfills the general Calculus I prerequisites for Mathematics, Physics, Chemistry, Engineering and Mathematical/Computational Biology majors who matriculated prior to Fall 2012 and has been approved by University Studies Curriculum for students matriculating in Fall 2012 or later.
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 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.
Research
Research awards
- $ 13,000 awarded by Mathematical Association of America for Mixed Model Implicit and IMEX Runge-Kutta Methods
- $ 438,284 awarded by Office of Naval Research for UMassD MUST IV: Learning Nonlinear Dynamical Systems from Sparse and Noisy Data: Applications to Signal Detection and Recovery
Research
Research interests
- 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
Dr. Chen is an Associate Professor in the Department of Mathematics at the University of Massachusetts Dartmouth. Prior to joining UMass Dartmouth six years ago, she served as a Postdoctoral Research Associate in the Computational and Applied Mathematics Group within the Computer Science and Mathematics Division at Oak Ridge National Laboratory. Before that, she held a departmental postdoctoral position in the Department of Mathematics at Iowa State University. Dr. Chen earned her PhD in Applied Mathematics from Brown University.
Her research specializes in scientific computing and numerical analysis, focusing on enhancing the accuracy, efficiency, and adaptability of numerical methods across a wide range of mathematical models. These methods are applied in areas such as fluid dynamics, kinetic theory, finance, and engineering mechanics. Dr. Chen's work is dedicated to developing and analyzing innovative algorithms that address complex real-world problems, making significant contributions to the fields of applied mathematics and computational science.