Interpretable Inference via Principled BNP Approaches in Biomedical Research and Beyond

Overview

The program will explore new directions in nonparametric Bayesian (BNP) theory and applications, focusing on applications in biomedical research problems. Specific themes in theory are: (1) complex data structures, dependent BNP models and quantification of the induced dependence; (2) efficient and scalable computational algorithms for BNP implementation; (3) summarizing BNP inference as a decision problem. And in applications: (1) real world evidence in clinical studies; (2) single cell RNA-seq data; (3) spatial transcriptomics; (4) subgroup analysis; (5) microbiome data; (6) network- and tree-structured data.