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Statistical Computing Webinar on June 16, 2022: Tian Zheng: Phase-Aligned Spectral Filtering for Decomposing Spatiotemporal Dynamics

  • 1.  Statistical Computing Webinar on June 16, 2022: Tian Zheng: Phase-Aligned Spectral Filtering for Decomposing Spatiotemporal Dynamics

     
    Posted 23 days ago
    (apologies for cross-posting)

    Time: June 16 1200 -1300(EST)

    Registration: https://uconn-cmr.webex.com/uconn-cmr/j.php?RGID=rb9eab23b97311885d6ca84ddfc31c9e3
    Please check your spam folder if an immediate
    response is not received after your registration.

    Title: Phase-Aligned Spectral Filtering for Decomposing Spatiotemporal Dynamics

    Abstract: Spatiotemporal dynamics is central to a wide range of
    applications from climatology, computer vision to neural sciences.
    From temporal observations taken on a high-dimensional vector of
    spatial locations, we seek to derive knowledge about such dynamics via
    data assimilation and modeling. It is assumed that the observed
    spatiotemporal data represent superimposed lower-rank smooth
    oscillations and movements from a generative dynamic system, mixed
    with higher-rank random noises. Separating the signals from noises is
    essential for us to visualize, model and understand these lower-rank
    dynamic systems. It is also often the case that such a lower-rank
    dynamic system has multiple independent components, corresponding to
    different trends or functionalities of the system under study. In this
    talk, I present a novel filtering framework for identifying lower-rank
    dynamics and its components embedded in a high-dimensional
    spatiotemporal system. It is based on an approach of structural
    decomposition and phase-aligned construction in the frequency domain.
    In both our simulated examples and real data applications, we
    illustrate that the proposed method is able to separate and identify
    meaningful lower-rank movements while existing methods fail.

    Short bio: Tian Zheng is Professor and Department Chair of Statistics
    at Columbia University. She develops novel methods for exploring and
    understanding patterns in complex data from different application
    domains. Her current projects are in the fields of statistical machine
    learning, spatiotemporal modeling, and social network analysis.
    Professor Zheng's research has been recognized by the 2008 Outstanding
    Statistical Application Award from the American Statistical
    Association (ASA), the Mitchell Prize from ISBA, and a Google research
    award. She became a Fellow of the American Statistical Association in
    2014. Professor Zheng is the recipient of the 2017 Columbia
    Presidential Award for Outstanding Teaching. From 2018 to 2020, she
    was the chair-elect, chair, and past-chair for ASA's section on
    Statistical Learning and Data Science.


    Linglong Kong, Webinar Committee Chair
    On behalf of the webinar committee at ASA Statistical Computing Section
    Mine Cetinkaya-Rundel, Kun Chen, Usha Govindarajulu, Linglong Kong,
    Jun Yan, and Hua Zhou