About
Sigal Gottlieb joined UMass Dartmouth in 1999 and is currently a Chancellor Professor in the Mathematics department. Her area of research is in computational and applied mathematics. Her work has been continually funded by the Air Force Office of Scientific Research (AFOSR) and the National Science Foundation (NSF). She is a Fellow of the Society of Industrial and Applied Mathematics and of the Association for Women in Mathematics.
Dr. Gottlieb was one of the founders and founding director of the Center for Scientific Computing and Data Science Research, the hub for computational science research at UMass Dartmouth and aims to support faculty doing computational research at UMass Dartmouth and promote internationally recognized computational research that advances the fields of modern applied science, data-driven and data science algorithms. She has led several successful equipment proposals for large-scale computing clusters that support the research of CSCDR affiliates.
In related activities, she was instrumental in developing new academic programs, including the EAS doctoral program and the Data Science BS and MS programs. Finally, Dr. Gottlieb has served in the Research, Scholarship, and Innovation committee since its inception, and as chair for the past two academic years.
Research areas
- Strong stability-preserving time-stepping methods for hyperbolic PDEs
- Positivity preserving and asymptotic preserving time evolution methods for kinetic equations with hyperbolic limits
- Error inhibiting time-discretizations
- Developing energy and power-efficient numerical methods
- Weighted essentially non-oscillatory methods for problems with sharp gradients and shocks
- Reduced order methods in a collocation setting
- Spectral collocation methods
My research interests lie primarily in the development of numerical algorithms for the simulation of hyperbolic PDEs. I am best known for my research in strong stability preserving (SSP) time discretizations. These comprise many types of time-evolution methods including Runge–Kutta methods, GLMs, multi-derivative methods, and integrating factor methods. Such methods can be explicit or implicit, and semi-implicit (IMEX) extensions have also been useful. I am also interested in spatial discretization techniques such as spectral and pseudospectral methods and WENO methods. More recently, I have been developing reduced basis methods in a collocation context.