The rapid expansion of multi-omics, clinical, and longitudinal health data is reshaping the landscape of personalized medicine. However, the high dimensionality, heterogeneity, and multi-modality of these data require advanced biostatistical and artificial intelligence (AI) methodologies capable of robust integration, interpretation, and clinical translation. In many biomedical applications, time-to-event outcomes such as overall survival, disease recurrence, or progression-free survival represent key clinical endpoints, further increasing methodological complexity. This session aims to bring together researchers working at the interface of biostatistics, machine learning, computational biology, survival analysis and precision health to discuss innovative methodological developments and real-world applications.