Organizer: Weilin Li, Mark Magsino
When: Tuesdays @ 2pm
Where: Math 1311

Archives: 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017

  • Organizational meeting

    Speaker: () -

    When: Tue, August 29, 2017 - 2:00pm
    Where: Kirwan Hall 1311
  • Super-resolution without minimum separation assumptions

    Speaker: Weilin Li (UMD) -

    When: Tue, September 12, 2017 - 2:00pm
    Where: Kirwan Hall 1311
  • Distributed Noise Shaping of Signal Quantization

    Speaker: Kung-Ching Lin (UMD) -

    When: Tue, September 19, 2017 - 2:00pm
    Where: Kirwan Hall 1311

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    Abstract: Quantization theory is an integral part of signal processing, focusing on the discrete nature of data storage in electronic devices and the effect of that. It stems from the needs to navigate the errors occurred from the numerous physical constraints during both sampling and reconstruction. This talk will give a quick exhibition of the development on this theory and discuss about a specific quantization scheme: Distributed noise shaping. Such scheme is robust against the usual physical constraints and has near-optimal error decay rate, making it a favorable choice.
  • Frames -- two case studies: ambiguity and uncertainty

    Speaker: John Benedetto (UMD) -

    When: Tue, September 26, 2017 - 2:00pm
    Where: Kirwan Hall 1311
  • Measures with locally finite support and spectrum

    Speaker: Chenzhi Zhao (UMD) -

    When: Tue, October 3, 2017 - 2:00pm
    Where: Kirwan Hall 1311
  • Multiscale analysis of data and functions in high dimension

    Speaker: Stefano Vigogna (JHU) -

    When: Tue, October 17, 2017 - 2:00pm
    Where: Kirwan Hall 1308
  • Topological Integral Transforms with Applications to Sensing

    Speaker: Robert Ghrist (UPenn) -

    When: Tue, October 24, 2017 - 2:00pm
    Where: 3206 Kirwan Hall

    View Abstract

    Abstract: This talk will outline a topological approach to constructing novel integral transforms. The basis for these methods is a blend of combinatorial and homological methods fused into an integration theory with respect to Euler characteristic. Using this as a starting point, several integral transforms will be described with applications given to problems of data aggregation over networks of sensors.
  • Image Space Embeddings for Data Visualization and Processing

    Speaker: Nate Strawn (Georgetown) -

    When: Tue, October 31, 2017 - 2:00pm
    Where: Kirwan Hall 1311

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    Abstract: We propose several extensions of PCA to isometrically embed arbitrary Euclidean datasets into high-dimensional spaces of images. In particular, this procedure produces a "visually coherent" icon for each data point. Such embeddings provide an interesting tool for Exploratory Data Analysis, and also allow us to apply mature techniques from Image Processing and Computer Vision to arbitrary datasets. We discuss theory, algorithms, and applications to Dictionary Learning, Deep Learning, and Topological Data Analysis.
  • Optimal coherence from finite group actions

    Speaker: Joey Iverson (UMD) -

    When: Tue, November 14, 2017 - 2:00pm
    Where: Kirwan Hall 1311

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    Abstract: In applications such as compressed sensing and quantum information theory, it is critically important to find examples of frames whose vectors are spread apart in the sense of having wide angles between them, as measured by the coherence. This is an old problem, going back at least to the work of van Lint and Seidel in the 1960s, and it remains an active and challenging area of research today. In this talk we will present a new recipe for converting transitive actions of finite groups into tight frames, many of which have optimal coherence. The main idea is to use an association scheme as a kind of converter to pass from the discrete world of permutation groups into the continuous setting of frames. This process is easy to implement in a program like GAP. We will present several examples of optimally coherent frames produced in this way, including the first infinite family of equiangular tight frames with Heisenberg symmetry. (These are not SIC-POVMs, but they appear to be related.)