Structured data foundation models are an emerging area of research undergoing rapid growth, yet they still remain critically under-explored relative to image and text modalities. So far, the different structured data sub-communities have had little opportunity to come together and share insights about how to build foundation models for structured data. Yet, strong synergies exist across modalities since models share similar pre-training and in-context learning paradigms. Furthermore, models trained on one modality can also demonstrate promising predictive performance in another. This workshop brings together the tabular and time series communities to jointly discuss foundation models for structured data, enabling the communities to capitalize on their synergies. We aim for advancements in foundation models that unify structured data modalities, addressing challenges of scalability and generalization across real-world applications. This emerging field promises to transform how we approach structured data analysis and drive new opportunities across various domains.