Modern data sets usually present multiple levels of heterogeneity, some apparent such as the necessity of combining trees, graphs, contingency tables and continuous covariates, others concern latent factors and gradients. The biggest challenge in the analyses of these data comes from the necessity to maintain and percolate uncertainty throughout the analyses. I will present a completely reproducible workflow that combines the typical kernel multidimensional scaling approaches with Bayesian nonparametrics to arrive at visualizations that present honest projection regions.

This talk will include joint work with Kris Sankaran, Julia Fukuyama, Lan Huong Nguyen, Ben Callahan, Boyu Ren, Sergio Bacallado, Stefano Favaro, Lorenzo Trippa and the members of Dr Relman's research group at Stanford.