Modern imaging now allows us to observe living systems across molecular, cellular, and tissue scales with unprecedented precision, yet our ability to extract mechanistic understanding to “learn the rules of life” from these data remains limited. Unlike molecular “omics” data, imaging captures continuous spatial and temporal information—shapes, motions, forces, concentrations and chemical potentials—that are inherently complex to represent, analyze, and interpret. The challenge lies in transforming this deluge of high-dimensional, dynamic spatiotemporal data into quantitative models, and linking these to other multi-modal data (e.g. proteomics, genomics, metabolomics), to reveal the governing principles of biological organization and dynamics across scales.

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The goal of the program is to accelerate the development of image-based scientific machine learning for biological dynamics. Such approaches will enable “learning the laws” of biology directly from experimental images and movies, yielding new insight into how complex forms and behaviors emerge and evolve from the scales of sub-cellular structures to whole organs. Building shared benchmarks, interpretable methodologies, open tools, and cross-disciplinary collaborations will accelerate discovery and lay the foundation for a new era of AI-enabled scientific discovery for theory in biology across scales. We aim to (rapidly) bring together physicists, computer scientists, mathematicians and biologists to build on the current momentum in this evolving field.