We build context-aware foundation models for regulatory genomics by leveraging large-scale sequence, epigenomic, transcriptomic, and perturbation datasets. Our goal is to develop context-aware models that can learn generalizable representations of gene regulation, predict molecular phenotypes across cell types and conditions, and enable downstream tasks such as genetic variant effect quantification, regulatory element discovery, and phenotype prediction.