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faculty

Scott Field, PhD

Associate Professor

Mathematics

Curriculum Vitae
Research Website

Contact

508-999-8281

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Liberal Arts 394E

Education

2011Brown UniversityPhD
2006University of RochesterBS

Teaching

Programs

Teaching

Courses

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