Single-cell multi-omics data integration aims to reduce the omics difference while keeping the cell type differences and aiding the exploration of the connections across epigenetic, transcriptomic and genetic changes. In this work, we attempt to integrate various singe-cell transcriptomic and epigenetic data modalities coming from single-cell RNA-Seq, single-cell ATAC-Seq and single-cell Cut&Tag experiments. The initial data matrices deriving from these techniques have high dimensionality, therefore the joint integrations and the downstream explorations by supervised and unsupervised machine learning tools are computationally challenging. Our goal with this proposal is to request computational power (including GPU specialized for complex calculations) to accomplish the in silico workflow of our multi-omics screen.