Jacob Bien (University of Southern California)

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Title: Mixture of Multivariate Regressions Modeling for Oceanographic Flow Cytometry Data

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Abstract: Although microscopic, phytoplankton in the ocean are extremely important to all of life and are together responsible for as much photosynthesis as all plants on land combined.  Today, oceanographers are able to collect flow cytometry data in real time while onboard a moving ship, providing them with a fine-scale resolution of the distribution of phytoplankton across thousands of kilometers of the ocean.  We present a novel sparse mixture of multivariate regressions model to estimate the time-varying phytoplankton subpopulations while simultaneously identifying the specific environmental covariates that are predictive of the observed changes to these subpopulations.  We also propose a new mixture model based on trend filtering that can accommodate settings in which environmental covariates are not available.

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