A grand challenge in the genome sciences is identifying functionally relevant variants and interpreting their biological impacts on cellular systems. A growing body of work in the biomedical sciences generates and analyzes omics data; our work contributes to these efforts by focusing on the integration of different omics data types to bring mechanistic insights to the multi-scale nature of cellular processes influenced by genetic and molecular variation. My lab develops methods that accelerate the clarity and utility of omics data in biomedical science to link genetic and molecular variation to phenotype in cellular systems. The models we develop shine light on which genomic variants are most relevant to cell functioning and how they impact cellular processes like transcription, translation, and drug response. Molecular variants we study range from single nucleotide polymorphisms to focal oncogene amplifications. We integrate a wide array of experimental techniques, including cytogenetics, fluorescence microscopy, long-read sequencing, and single-cell transcriptomics, with computational methodologies, such as machine learning, data reduction and clustering, computational biology, and bioinformatics. Our overarching goal is to identify functionally relevant variants to advance precision medicine and targeted therapies.