This talk will give an overview of two algorithms for aligning single cell multi-omics data. The first algorithm, SCOT (Single Cell alignment using Optimal Transport), aims to align cells from different multi-omics measurements, such as gene expression, chromatin accessibility, and DNA methylation data. This approach is based on entropy-regularized Gromov-Wasserstein optimal transport and attempts to conserve pairwise distances of nearby data points. We show the efficacy of this algorithm using synthetic data and two experimental co-assay data sets. Next, we will present AGW (Augmented Gromov-Wasserstein), a novel formulation that allows us to align both samples (cells) and features (genes) simultaneously and effectively across different single cell datasets. We show the improved performance of this formulation and its ability to align features and provide supervision on either sample or feature level for challenging single cell alignment tasks.