The purpose of the meetings is to explore deeper results or recent topics in information theory. The goal is to broaden the knowledge of participants, especially students. Research ideas may also be inspired.
We will choose a set of ideas within information theory, and loosely related to information theory, as they relate to research projects in ESSRL and elsewhere on Washington University's campus. Participants will be called on to present ideas. These should be relatively polished research topics or papers from the literature. The definition of information theory will be modified as needed to accomodate each paper studied.
Robust uncertainty principles, universal encoding of signals and compressive sensing. Applied statistics on signal processing, learning and system performance analysis. Information geometry and related inference algorithms.
Random point processes, dose reduction X-Ray CT imaging, intensity modulated radiation therapy (IMRT), time warping, majorization and its application, density estimation, stochastic complexity, wavelet and its applications.
We will focus on the analysis of data dimension, adaptive data extraction and algorithms for them.
There are three main areas for the summer: image registration, image reconstruction algorithms, and quantum information theory.
There will be two separate themes of the research covered this semester, implying that we will often separate into two groups. One group will be more focused on the statistics of natural imagery. One will work through a set of more general topics.
Information geometry, and expectation maximization algorithm.
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