faculty
Scott Field, PhD
Associate Professor
Mathematics
Research Website
Contact
508-999-8281
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Spruce Hall 0174
Education
| 2011 | Brown University | PhD |
| 2006 | University of Rochester | BS |
Teaching
Programs
Programs
Teaching
Courses
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.
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.
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.
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.
Scientific machine learning algorithms for computational science and engineering. Topics may include physics-informed neural networks, neural dynamical systems, AI-based surrogate models, signal detection with convolutional neural networks, learning nonlinear continuous operators, neural turbulence models, optimization algorithms, simulation-based Bayesian inference, and more. Python will be the primary language. Emphasis on real-world applications, covering high-performance computing with multi-core and GPU acceleration.
Research
Research awards
- $ 189,022 awarded by NATIONAL SCIENCE FOUNDATION for Collaborative Research: CDS&E: Data-Driven Discovery of Neural ODE Dynamics, Astrophysical Models, and Orbits (Neural ODE DynAMO)
- $ 349,101 awarded by National Science Foundation for Developing High Order Stable and Efficient Methods for Long Time Simulations of Gravitational Waveforms
- $ 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
- $ 650,000 awarded by National Science Foundation for 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
Latest from Scott
Mentioned in
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- Feb 6, 2024 NASA, ESA move forward on science supported by UMass Dartmouth faculty
- Oct 20, 2023 UMassD PhD students and faculty gain new insights on binary black holes
- Jan 31, 2022 UMass Dartmouth professor and students discover fast-moving black hole