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faculty
Zheng Chen, PhD
Assistant Professor
Mathematics
Contact
508-999-9236
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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
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 introduction to the main concepts and techniques of college algebra. Topics include: linear, quadratic, exponential and logarithmic functions, as well as modeling of data using functions. This is the first semester of the college math sequence designed for students interested in Biology and Life Sciences. This course fulfills the general education core requirements for Biology and Life Sciences majors who matriculated prior to Fall 2012 and has been approved by University Studies Curriculum for students matriculating in Fall 2012 or later.
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.
An introduction to constrained and unconstrained optimization. Numerical optimization is an essential tool in a wide variety of applications. The course covers fundamental topics in unconstrained optimization and also methods for solving linear and nonlinear constrained optimization problems.
An introduction to constrained and unconstrained optimization. Numerical optimization is an essential tool in a wide variety of applications. The course covers fundamental topics in unconstrained optimization and also methods for solving linear and nonlinear constrained optimization problems.
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