We recently found that any continuous covariance for time-series data, no matter how intricate, can be approximated arbitrarily well in terms of a well-behaved parametric family of linear projections of linear stochastic dynamical systems. This family makes efficient exact inference a breeze, even for millions of time-points. Applied to ATAC-seq data, this machinery infers smooth representations that encode how chromatin accessibility varies (1) along the one-dimensional topology of each chromosome and (2) throughout the diversity of cells.