Dr. Kowal's research focuses on statistical methods for massive data sets with complex dependence structures, including functional, time series, and spatial data. For many applications, these dependence structures appear concurrently. He develops and studies hierarchical Bayesian models, which provide both sufficient model flexibility to tackle complex problems as well as mechanisms for regularization to prevent overfitting.
Publications/Creative Works
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