Inverse problems

Tuesday, March 15, 2016 - 3:30pm - 4:00pm
Harvey Banks (North Carolina State University)
We investigate the feasibility of quantifying properties of a composite dielectric material through the reflectance, where the permittivity is described by the Lorentz model in which an unknown probability measure is placed on the model parameters. We summarize the computational and theoretical framework (the Prohorov Metric Framework) developed by our group in the past two decades for nonparametric estimation of probability measures using a least-squares method, and point out the limitation of the existing computational algorithms for this particular application.
Wednesday, April 13, 2011 - 2:00pm - 3:00pm
Peter Kitanidis (Stanford University)
The subsurface is where most of the available freshwater is stored; in the United States, groundwater is the primary source of water for over 50 percent of Americans, and roughly 95 percent for those in rural areas. Cleaning up the surface from industrial and nuclear wastes is quite challenging. A major impediment in studying processes in the subsurface and in managing resources is that it is difficult to achieve accurate and reliable imaging, i.e., identification of properties, of geologic formations.
Monday, April 12, 2010 - 10:00am - 10:45am
François Primeau (University of California)
Keywords: Transit-time distribution (TTD); tracer transport; inverse problem; maximum-entropy deconvolution
Monday, March 26, 2012 - 2:45pm - 3:30pm
Venkat Chandrasekaran (University of California, Berkeley)
Deducing the state or structure of a system from partial, noisy measurements is a fundamental task throughout the sciences and engineering. The resulting inverse problems are often ill-posed because there are fewer measurements available than the ambient dimension of the model to be estimated.
Friday, November 18, 2011 - 8:30am - 9:30am
Peter Doerschuk (Cornell University)
Single-particle cryo electron microscopy provides
images of biological macromolecular complexes with
spatial sampling on the order of 1-2 Angstrom.
Combining on the order of 100,000 such images can result
in 3-D reconstructions of the electron scattering
intensity of the complex with a spatial resolution as
fine as 4-5 Angstrom. Due to damage in the imaging
process, each complex is imaged only once and therefore
having a homogeneous ensemble of complexes is
important. Algorithms and results will be presented
Wednesday, November 16, 2011 - 3:15pm - 4:15pm
Rebecca Willett Lu (Duke University)
Sparse decomposition methods are effective tools in a myriad biomedical inverse problems. However, in many settings reconstruction is only an intermediate goal preceding additional quantitative analysis. For instance, we may wish to classify tissue types in microscope images or identify tumors or lesions based on computed tomography data. This talk describes how sparse image decomposition methods can be used in conjunction with multiscale set estimation methods to improve subsequent quantitative analyses on large medical datasets.
Wednesday, June 25, 2008 - 3:30pm - 4:30pm
Steven Cox (Rice University)
Thursday, February 9, 2006 - 1:30pm - 2:30pm
Eugene Vendrovsky (Rhythm & Hues Studios, Inc.)
I shall talk about some practical problems of special effects /
animation production that could be broadly defined as Inverse Problems:
data is recovered from observed images rather than generated on user
specification. Most of Computer Vision tasks in movie production,
opposite to most Computer Graphics tasks, fall in Inverse Problems
category. The problems I will speak of are: rigid body surveyless,
articulated body (as hierarchy of rigid objects), face and flexible
surface tracking (markers based, markerless, featureless) and
Friday, November 11, 2005 - 9:00am - 10:00am
Sarah Patch (University of Wisconsin)
Abstract is in pdf format.
Thursday, November 10, 2005 - 2:00pm - 3:00pm
George Michailidis (University of Michigan)
In this talk we examine a class of inverse problems that arise on
graphs. We provide a review of
recent developments, including design aspects for identifiability
purposes, inference issues and
applications to computer networks.


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