Sigal Gottlieb

faculty

Sigal Gottlieb, PhD

Chancellor Professor

Mathematics

Curriculum Vitae
Research Website

Contact

508-999-8205

508-910-6917

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Liberal Arts 394D

Education

1998Brown UniversityPhD
1995Brown UniversityScM
1993Brown UniversityScB

Teaching

  • Numerical Analysis
  • Scientific Computing
  • Differential Equations

Teaching

Programs

Teaching

Courses

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.

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.

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.

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.

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.

Research

Research awards

Research

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.

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