Paul Constantine is an assistant professor in the Department of Computer Science at University of Colorado Boulder. He received his PhD from Stanford's Institute for Computational and Mathematical Engineering and was awarded the John von Neumann Postdoctoral Fellowship at Sandia National Labs. His interdisciplinary research interests include parameter reduction and uncertainty quantification for computational science and engineering models. For more information, see www.cs.colorado.edu/~paco3637
parameter reduction, dimension reduction, active subspaces, uncertainty quantification, scientific machine learning, computational science and engineering, scientific computing, numerical computing
CSCI 3656 - Numerical Computation
Fall 2018 / Fall 2019 / Fall 2020 / Fall 2021 / Fall 2022
Covers development, computer implementation, and analysis of numerical methods for applied mathematical problems. Explores topics such as floating point arithmetic, numerical solution of linear systems of equations, root finding, numerical interpolation, differentiation, and integration.
CSCI 5606 - Principles of Numerical Computation
Highlights computer arithmetic, solution of linear systems, least-squares approximations, nonlinear algebraic equations, interpolation, and quadrature. Recommended prerequisites: CSCI 3656 and three semesters of calculus or equivalent.
CSCI 5646 - Numerical Linear Algebra
Spring 2019 / Spring 2021
Offers direct and iterative solutions of linear systems. Also covers eigen value and eigenvector calculations, error analysis, and reduction by orthogonal transformation. A sound knowledge of basic linear algebra, experience with numerical computation, and programming experience is required.
CSCI 7000 - Current Topics in Computer Science
Spring 2018 / Spring 2019
Covers research topics of current interest in computer science that do not fall into a standard subarea. May be repeated up to 8 total credit hours.