Learning from Time-Series for Health

Time-series data underpin modern healthcare, spanning electronic health records, physiological waveforms, wearables, and population trends, yet their unique characteristics—including uncertain ground truth, quasi-periodic physiological motifs, and non-semantic timepoints—demand specialized machine learning approaches. While recent advances in foundation models, multimodal learning, and generative methods show promise, significant challenges remain in causality, interpretability, and deployment. This workshop unites researchers across health time-series domains (from wearables to clinical systems) to address shared challenges through: (1) cross-domain discussion, (2) diverse industry/academic perspectives (featuring Google, Oura, Apple and 5 institutions), and (3) community engagement via posters, talks, and panels. By fostering cross-domain collaboration on physiological-aware methods, we aim to bridge the gap between cutting-edge ML and real-world healthcare impact.