Presenter: Dr. Xun Tang, Stanford University Description: This talk covers an efficient numerical approach for compressing a high-dimensional discrete distribution function into a non-negative tensor train (NTT) format. The two settings we consider are variational inference and density estimation, whereby one has access to either the unnormalized analytic formula of the distribution or the samples […]
Presenter: Snigdha Panigrahi, Associate Professor, Department of Statistics, University of Michigan Description:Agglomerative hierarchical clustering is one of the most widely used approaches for exploring how observations in a dataset relate to each other. However, its greedy nature makes it highly sensitive to small perturbations in the data, often producing different clustering results and making it […]
Presenter: Guan’ao Yan, Assistant Professor, Michigan State University Description: Single-cell and spatial transcriptomics technologies now let us map cellular diversity and tissue organization at high resolution, but the computational methods built to analyze these data are difficult to evaluate in a rigorous, reproducible way. Two key barriers are the lack of realistic synthetic data with […]