When: Friday, February 20, 11:00 AM Where: Pharmacy 240 Abstract In this talk, we explore efficient Gaussian process surrogate modeling in two distinct contexts: bandit optimization and blackbox posterior approximation. For optimization, we propose novel noise-free Bayesian optimization strategies that incorporate a random exploration step to enhance the accuracy of Gaussian process surrogate models. The […]

When: Wednesday, February 18, 12:00 PM Where: Avedisian 105 Abstract Neuroimaging data present fundamental statistical challenges: they are high-dimensional and exhibit complex structures. In this talk, I present Bayesian methods developed for functional data and matrix-valued data motivated by neuroimaging applications, emphasizing interpretability, scalability, and uncertainty quantification. I first introduce Bayesian methods developed for two […]

When: Friday, February 6, 3:00 PM Where: Tyler 055 Abstract In this talk, we will first discuss the proposed α-separability for functional data. Functional data consist of random samples observed over a continuum, such as curves over a time range. These data often exhibit two kinds of variation: amplitude variation in the vertical direction and […]

When: Wednesday, February 4, 10:00 AM Where: Tyler 053 Abstract The block-model family has four popular network models (SBM, DCBM, MMSBM, and DCMM). A fundamental problem is how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with […]