APPM Department Colloquium - Doug Nychka

Feb. 10, 2017

Event Description: Doug Nychka, National Center for Atmospheric Research (NCAR), Boulder, CO Large and non-stationary spatial fields: Quantifying uncertainty in the pattern scaling of climate models Pattern scaling has proved to be a useful way to extend and interpret Earth system model (i.e. climate) simulations. In the simplest case the...

APPM Department Colloquium - Ana Maria Rey

Feb. 3, 2017

Event Description: Ana Maria Rey, Joint Institute for Laboratory Astrophysics, University of Colorado Boulder Building with Crystals of Light and Quantum Matter: From clocks to computers Understanding the behavior of interacting electrons in solids or liquids is at the heart of modern quantum science and necessary for technological advances. However,...

APPM Department Colloquium - Xuemin Tu

Jan. 27, 2017

Event Description: Xuemin Tu, Department of Mathematics, University of Kansas Nonoverlapping Domain Decomposition Methods for Saddle Point Problems Two widely used nonoverlapping domain decomposition methods BDDC (Balancing domain decomposition by constraints) and FETI-DP (Dual-primal finite element tearing and interconnecting) are studied for the systems of linear equations arising from the...

APPM Department Colloquium - Steffen Borgwardt

Jan. 20, 2017

Event Description: Steffen Borgwardt, Department of Mathematical and Statistical Sciences, University of Colorado - Denver Operations Research in Land Exchange With geometric modeling techniques, one can represent the feasible solutions of problems in operations research as objects in high-dimensional space. The properties of these objects reveal information about the underlying...

Applied Mathematics Colloquium

Dec. 9, 2016

Event Description: A weekly presentation of current research in the field of Applied Mathematics, suitable for most audiences. Location Information: Main Campus - Engineering Classroom Wing ( View Map ) 1111 Engineering DR Boulder, CO Room: 245 Contact Information: Name: Ian Cunningham Phone: 303-492-4668 Email:

Applied Mathematics Colloquium - Will Kleiber

Dec. 2, 2016

Event Description: Will Kleiber, Department of Applied Mathematics, University of Colorado Boulder Multivariate Random Fields Most modern spatial datasets involve multiple variables that can exhibit complex cross-process dependencies. We review some classic approaches to building statistical models for multivariate spatial data, nearly all of which rely on specifying cross-covariance functions...

Applied Mathematics Colloquium - Yi Qiang

Nov. 11, 2016

Event Description: Yi Qiang, The Earth Lab and Dept. of Geography, University of Colorado Boulder From Time to Time: Novel Representations of Time for Visual Analytics How to represent time is a fundamental question in many disciplines. Conventionally, time intervals are represented as linear segments in a one-dimensional space, which...

Applied Mathematics Colloquium - Alireza Doostan

Nov. 4, 2016

Event Description: Alireza Doostan, Department of Aerospace Engineering Sciences, University of Colorado Boulder Uncertainty Quantification Using Low-fidelity Data Realistic analysis and design of multi-disciplinary engineering systems require not only a fine understanding and modeling of the underlying physics and their interactions but also recognition of intrinsic uncertainties and their influences...

Applied Mathematics Colloquium - Sean O'Rourke

Oct. 28, 2016

Event Description: Sean O'Rourke, Department of Mathematics, University of Colorado Boulder Singular values and vectors under random perturbation Computing the singular values and singular vectors of a large matrix is a basic task in high dimensional data analysis with many applications in computer science and statistics. In practice, however, data...

Applied Mathematics Colloquium - Ben Herbst

Oct. 21, 2016

Event Description: Ben Herbst, Division of Mathematical Sciences, Stellenbosch University Models in Applied Mathematics I’ll discuss three different but not necessarily mutually exclusive, types of models: Deterministic models (for want of a better term), neural networks and in particular deep neural networks, and probabilistic models in particular probabilistic graphical models...