Scott Field, PhD

Assistant Professor

Mathematics

Curriculum Vitae
Research Website

508-999-8318

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Liberal Arts 394C

Education

2011Brown UniversityPhD
2006University of RochesterBS

Teaching

Programs

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.

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.

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.

Doctoral thesis proposal development based on technical writing process, data interpretation, experimental design. Students who successfully complete the course will be able to assess information from the primary scientific literature, formulate scientific questions (hypotheses), and generate an experimental plan to help validate or nullify their hypothesis. Students will demonstrate a command of oral and written communication skills by completing this 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.

Investigations of a fundamental and/or applied nature representing an original contribution to the scholarly research literature of the field. PhD dissertations are often published in refereed journals or presented at major conferences. A written dissertation must be completed in accordance with the rules of the Graduate School and the College of Engineering. Admission to the course is based on successful completion of the PhD comprehensive examination and submission of a formal proposal endorsed by the student's graduate committee and submitted to the EAS Graduate Program Director.

Investigations of a fundamental and/or applied nature representing an original contribution to the scholarly research literature of the field. PhD dissertations are often published in refereed journals or presented at major conferences. A written dissertation must be completed in accordance with the rules of the Graduate School and the College of Engineering. Admission to the course is based on successful completion of the PhD comprehensive examination and submission of a formal proposal endorsed by the student's graduate committee and submitted to the EAS Graduate Program Director.

Prerequisite: Graduate standing; approval by advisor, graduate program director and department chairperson. Experiential learning in conjunction with an industrial or governmental agency project under the joint supervision of an outside sponsor and a faculty advisor. To be eligible, a student should have completed at least half of his/her program of study. A detailed project proposal must be prepared by the student for departmental approval prior to the start of the project. Upon completion, student must submit a report on the experience and make a short presentation to his/her graduate committee. This course may be used to satisfy one 3-credit graduate technical elective course.

Research

Research Awards

  • $650,000.00 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
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