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faculty
Sigal Gottlieb, PhD
Chancellor Professor
Mathematics
Research Website
Education
| 1998 | Brown University | PhD |
| 1995 | Brown University | ScM |
| 1993 | Brown University | ScB |
Teaching
- Numerical Analysis
- Scientific Computing
- Differential Equations
Programs
Programs
- Data Science BS, BS/MS
- Data Science Graduate Certificate
- Data Science MS
- Engineering and Applied Science PhD
- Mathematics BA, BS
Courses
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.
Introduction to the diverse ethical concerns, challenges and responsibilities that arise when engaging in scientific research. Students will have opportunities to reflect upon and discuss their own ethical constructs in the face of practical ethical dilemmas.
A seminar series on interdisciplinary research topics by prominent speakers in EAS fields and student presentations on research in progress. May be repeated for credit.
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.
Theory and computer-oriented practice in obtaining numerical solutions of various problems. Topics include stability and conditioning, nonlinear equations, systems of linear equations, interpolation and approximation theory.
Numerical methods for solving parabolic, hyperbolic, and elliptic partial differential equations. The course will emphasize the concepts of consistency, convergence and stability. Topics include: implicit and explicit methods, truncation error, Von Newmann stability analysis, and the Lax equivalence theorem.
Development, analysis, and implementation of numerical methods to approximate solutions of partial differential equations. An advanced study of numerical methods for approximating the solution of partial differential equations. Topics may include: numerical methods for hyperbolic PDEs; finite element methods; discontinuous Galerkin methods; spectral methods; pseudo spectral (collocation) methods; radial basis function methods; numerical methods for time-stepping of PDEs
Numerical methods for solving parabolic, hyperbolic, and elliptic partial differential equations. The course will emphasize the concepts of consistency, convergence and stability. Topics include: implicit and explicit methods, truncation error, Von Newmann stability analysis, and the Lax equivalence theorem.
Development, analysis, and implementation of numerical methods to approximate solutions of partial differential equations. An advanced study of numerical methods for approximating the solution of partial differential equations. Topics may include: numerical methods for hyperbolic PDEs; finite element methods; discontinuous Galerkin methods; spectral methods; pseudo spectral (collocation) methods; radial basis function methods; numerical methods for time-stepping of PDEs
Research
Research awards
- $ 349,101 awarded by National Science Foundation for Developing High Order Stable and Efficient Methods for Long Time Simulations of Gravitational Waveforms
- $ 52,850 awarded by National Science Foundation for Development of Efficient Black Hole Spectroscopy and a Desktop Cluster for Detecting Compact Binary Mergers
- $ 130,113 awarded by U.S. Department of the Air Force for Computationally and Energy Efficient Mixed Precision and Mixed-model Numerical Methods
- $ 145,000 awarded by Michigan State University | U.S. Department of Energy for Center for Hierarchical and Robust Modeling of Non- Equilibrium Transport (CHaRMNET)
- $ 296,555 awarded by The National Science Foundation for Reduced Basis Enhancements of Neural Networks and Their Application to Quantum Materials Simulation
Research interests
- My research interests are numerical analysis and scientific computing. Specifically, I am interested in high-order numerical methods for simulation of hyperbolic PDEs with shocks.
- WENO, spectral, and pseudo spectral methods, as well as strong stability preserving time discretizations.
- Reduced basis methods for solving PDEs with many parameters.
- Weighted essentially non-oscillatory methods
Select publications
See curriculum vitae for more publications
- Sigal Gottlieb, David Ketcheson, and Chi-Wang Shu (2011).
Strong Stability Preserving Runge-Kutta and Multistep Time Discretizations - Jan Hesthaven, Sigal Gottlieb, and David Gottlieb (2007).
Spectral Methods for Time-Dependent Problems
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, and 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 the development of 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.