Scott Field

Scott Field is an Associate Professor in the Department of Mathematics at the University of Massachusetts Dartmouth, where he co-directs the Engineering & Applied Science PhD program. His research integrates gravitational wave data science, computational relativity, and high-performance computing to model astrophysical phenomena like black hole mergers.

His primary research areas include developing discontinuous Galerkin methods for efficient waveform computation, creating surrogate models to accelerate gravitational wave predictions, and applying machine learning to astrophysical inverse problems. His work supports gravitational wave observatories like LIGO and LISA.

He has secured substantial research funding, including NSF and ONR grants exceeding $1.6 million. Notable projects focus on neural ODE dynamics for orbital modeling and high-order methods for long-duration waveform simulations.

As co-developer of the GWSurrogate Python package, he enables efficient gravitational wave data analysis. He collaborates with the Simulating eXtreme Spacetimes (SXS) consortium and contributes to the LISA mission through waveform modeling.