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
- 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.
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.
Topics in high performance computing (HPC). Topics will be selected from the following: parallel processing, computer arithmetic, processes and operating systems, memory hierarchies, compilers, run time environment, memory allocation, preprocessors, multi-cores, clusters, and message passing. Introduction to the design, analysis, and implementation, of high-performance computational science and engineering applications.
Topics in high performance computing (HPC). Topics will be selected from the following: parallel processing, computer arithmetic, processes and operating systems, memory hierarchies, compilers, run time environment, memory allocation, preprocessors, multi-cores, clusters, and message passing. Introduction to the design, analysis, and implementation, of high-performance computational science and engineering applications.
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.
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.
Topics in high performance computing (HPC). Topics will be selected from the following: parallel processing, computer arithmetic, processes and operating systems, memory hierarchies, compilers, run time environment, memory allocation, preprocessors, multi-cores, clusters, and message passing. Introduction to the design, analysis, and implementation, of high-performance computational science and engineering applications.
Research
Research Interests
- gravitational wave data science
- discontinuous Galerkin methods
- Large-scale Scientific Computation
- computational general relativity and fluid dynamics
- Numerical Analysis