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The dynamics of most biological phenomena—how they change over time—can often provide information that is not apparent when only a snapshot is considered. For example, the way the maternal immunome changes during pregnancy, rather than its specific state at any specific time, is indicative of ensuing pathology. Integrated temporal signals, across information layers, allow us to comprehensively describe a complex and dynamic biological process, and accurately predict its emergent properties. Inferring these dynamics, however, is not simple; in complex systems such as the human body, this requires understanding numerous organ systems, tissue types, and components, as well as their trajectories and interactions – naively, an intractable task. A reasonable assumption, proven useful in diverse scenarios, is that there are latent trajectories that largely govern these biological processes. Liat Shenhav designs and develops frameworks that combine longitudinal data analysis with statistical learning, to uncover the underlying dynamics that differentiate between normal and pathological conditions across biological systems.