Scott Field, PhD
Assistant Professor
Mathematics
Contact
508-999-8318
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Liberal Arts 394C
Education
| 2011 | Brown University | PhD |
| 2006 | University of Rochester | BS |
Teaching
Programs
Programs
- Data Science BS, BS/MS
- Master's in Data Science MS
- Engineering and Applied Science PhD
- Mathematics BA, BS
Teaching
Courses
Application of knowledge discovery and data mining tools and techniques to large data repositories or data streams. This project-based capstone course provides students with a framework in which students gain both understanding and insight into the application of knowledge discovery tools and principles on data within the student's cognate area. This course is intended for data science majors only.
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.
A calculus-based introduction to scientific computation, modeling, simulation and visualization using a variety of mathematics programming tools, scripting languages, and other software tools widely used by mathematicians. This course is project-driven and requires a strong background in mathematics. It is intended for students planning to take upper-level courses in applied or computational mathematics.
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.
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 Awards
- $650,000 Implementation of a Contextualized Computing Pedagogy in STEM Core Courses and Its Impact on Undergraduate Student Academic Success, Retention, and Graduation
Research
Research Interests
- gravitational wave data science
- discontinuous Galerkin methods
- Large-scale Scientific Computation
- computational general relativity and fluid dynamics
- Numerical Analysis