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Atlanta, GA | Posted: September 10, 2012
Summary Sentence:
The 2012 speaker is Dr. Emmanuel Candès from Stanford University.
Full Summary:
No summary paragraph submitted.
Emmanuel CandesThe 2012 speaker is Dr. Emmanuel Candès from Stanford University. He holds the Simons Chair in Mathematics and Statistics. His research areas include: compressive sensing, mathematical signal processing, computational harmonic analysis, multiscale analysis, scientific computing, stastistical estimation and detection, high-dimensional statistics. Applications to the imaging sciences and inverse problems. Other topics of recent interest include theoretical computer science, mathematical optimization, and information theory.
There will be two lectures. One (for a general audience) will be on September 10, at 4:25 pm, in Clough Commons, Room 144. Another one will be at 11:05 am on September 11 in Skiles 006.
Lecture 1: General Audience
This talk is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. In the second part of the talk, we present applications in computer vision. In video surveillance, for example, our methodology allows for the detection of objects in a cluttered background. We show how the methodology can be adapted to simultaneously align a batch of images and correct serious defects/corruptions in each image, opening new perspectives.
Lecture 2: Mathematics Lecture
This talks introduces a novel framework for phase retrieval, a problem which arises in X-ray crystallography, diffraction imaging, astronomical imaging and many other applications. Our approach combines multiple structured illuminations together with ideas from convex programming to recover the phase from intensity measurements, typically from the modulus of the diffracted wave. We demonstrate empirically that any complex-valued object can be recovered from the knowledge of the magnitude of just a few diffracted patterns by solving a simple convex optimization problem inspired by the recent literature on matrix completion. More importantly, we also demonstrate that our noise-aware algorithms are stable in the sense that the reconstruction degrades gracefully as the signal-to-noise ratio decreases. Finally, we present some novel theory showing that our entire approach may be provably surprisingly effective.
