Monday, May 21, 2018 - 3:15pm - 4:15pm
Emilie Purvine (Pacific Northwest National Laboratory)
When using sheaves to model real-world problems -- e.g., topological signal processing or information integration -- we must face up to the challenges that real data provide. In a sheaf-theoretical context, measured data, called assignments, are represented by members of objects in the data category. Assignments need not be sections, and indeed, as measured data, almost always require statistical descriptions and tolerances. Uncertainty can exist between disparate measurements generally, and even on the same observable, due to uncertainty, noise, or sensor malfunctions.
Tuesday, December 1, 2015 - 1:25pm - 2:25pm
Vipin Kumar (University of Minnesota, Twin Cities)
This talk will present an overview of research being done in a large interdisciplinary project on the development of novel data driven approaches that take advantage of the wealth of climate and ecosystem data now available from satellite and ground-based sensors, the observational record for atmospheric, oceanic, and terrestrial processes, and physics-based climate model simulations. These information-rich datasets offer huge potential for monitoring, understanding, and predicting the behavior of the Earth's ecosystem and for advancing the science of global change.
Wednesday, June 8, 2011 - 9:00am - 10:00am
Eldad Haber (University of British Columbia)
In recent years a new data collection approach has been proposed for geophysical
exploration. Rather than recording data for each source separately, sources are shot
simultaneously and the combined data is recorded.
The question we answer in this talk is, what should be the pattern of shots in order
to optimally recover the earth's parameters.
To answer the question we use experimental design methodology and show how
to efficiently solve the resulting optimization problem
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