Dr. Jodi L. Mead joined the faculty of Boise State University in 2000. She earned her B.S. from Syracuse University, and her M.A. and Ph.D. in Mathematics from Arizona State. During the Spring Semester of 2007 Dr. Mead was a Visiting Professor at Arizona State University, where she began developing the Chi-squared method for parameter estimation and uncertainty quantification with Rosemary Renaut. The idea for this method came from her experience working with people in the Oceanography Department at Oregon State University, and the Geosciences Department at Boise State University. Her other research interests include inverse methods, numerical analysis, applied partial differential equations; including regularization, data assimilation pseudospectral methods, Runge-Kutta methods, absorbing boundary conditions, data assimilation, and how they relate to problems in imaging, hydrology, geophysics, physical oceanography, and computational aeroacoustics, physical oceanography and the geosciences.
Articles
Discontinuous Parameter Estimates with Least Squares Estimators, Applied Mathematics and Computation (2013)
We discuss weighted least squares estimates of ill-conditioned linear inverse problems where weights are chosen...
Least Squares Problems with Inequality Constraints as Quadratic Constraints (with Rosemary A. Renaut), Linear Algebra and its Applications (2010)
Linear least squares problems with box constraints are commonly solved to find model parameters within...
A Newton Root-Finding Algorithm For Estimating the Regularization Parameter For Solving Ill-Conditioned Least Squares Problems (with Rosemary Renaut), Inverse Problems (2009)
We discuss the solution of numerically ill-posed overdetermined systems of equations using Tikhonov a-priori-based regularization....
A Priori Weighting for Parameter Estimation, Journal of Inverse and Ill-Posed Problems (2008)
We propose a new approach to weighting initial parameter misfits in a least squares optimization...
An Iterated Pseudospectral Method for Functional Partial Differential Equations (with Barbara Zubik-Kowal), Applied Numerical Mathematics (2005)
Chebyshev pseudospectral spatial discretization preconditioned by the Kosloff and Tal-Ezer transformation [10] is applied to...
Contributions to Books
Pseudospectral Iterated Method for Differential Equations with Delay Terms (with Barbara Zubik-Kowal), Lecture Notes in Computer Science (2004)
New efficient numerical methods for hyperbolic and parabolic partial differential equations with delay terms are...
Presentations
Mathematics Colloquium: Inverse Problems and Uncertainty Quantification, University of Idaho (2012)
Combining physical or mathematical models with observational data often results in an ill-posed inverse problem....
Solution of a Nonlinear System for Uncertainty Quantification in Inverse Problems, Eleventh Copper Mountain Conference on Iterative Methods (2010)
Non-smooth Solutions to Least Squares Problems, SIAM Conference on Applied Linear Algebra (2009)
In an attempt to overcome the ill-posedness or illconditioning of inverse problems, regularization methods are...