Machine Learning for Spatially Resolved High-dimensional Biology

Spatially resolved biology is rapidly transforming the study of tissues by measuring molecular activity while preserving spatial organization. Technologies such as spatial transcriptomics, spatial proteomics, and multiplex imaging now generate high-dimensional, multimodal data at cellular, tissue, and atlas scales. These data pose new machine learning challenges: representing irregular tissue geometry, integrating molecular and imaging modalities, modeling cell-cell communication, handling noise and sparsity, building interpretable and uncertainty-aware models, and designing reliable benchmarks. This workshop will bring together machine learning researchers, computational biologists, and experimental scientists to define spatially resolved high-dimensional biology as a core methodological problem for ML. It will focus on geometric and graph learning, multimodal representation learning, generative modeling, foundation models, biological inductive biases, and evaluation standards for spatial omics and tissue-scale data.