Tuesday, October 14, 2014 - 3:15pm - 4:05pm
Talea Mayo (Princeton University)
In this work, we use a physically based assessment to estimate the risk of hurricane storm surge at four sites along the U.S. North Atlantic coast. We estimate storm surge return levels statistically by forcing a hydrodynamic model with the wind and pressure field data of thousands of hurricanes. Rather than relying on the limited historical records, we force the model with synthetic hurricanes, which are generated from a statistical-deterministic model.
Tuesday, November 15, 2011 - 3:15pm - 4:15pm
W. Clem Karl (Boston University)
There has been an explosion of non-invasive biomedical sensing
modalities that have revolutionized our ability to probe the
biomedical world. Often decisions have to be made on the basis of
these increasingly high-dimensional observations. An example would be
the determination of cancer or stroke from indirect tomographic
projection measurements. The problem is frequently exacerbated by the
lack of labeled training samples from which to learn class models. In
many cases, however, there exists a latent low-dimensional sensing
Thursday, September 8, 2011 - 11:30am - 12:10pm
Thomas Hou (California Institute of Technology)
We introduce a sparse time-frequency analysis method for analyzing nonlinear and non-stationary data. This method is inspired by the Empirical Mode Decomposition method (EMD) and the recently developed compressed sensing theory. The main idea is to look for the sparsest representation of multiscale data within the largest possible dictionary consisting of intrinsic mode functions. We formulate this as a nonlinear optimization problem. Further, we propose an iterative algorithm to solve this nonlinear optimization problem recursively.
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