אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Clinical multivariate time series, such as 12-lead electrocardiograms provide essential information for diagnosis and patient monitoring. However, learning reliable models for these signals remains challenging because they exhibit complex temporal dynamics, strong dependencies between channels, limited labeled data, and physiological constraints that are often ignored by standard deep learning methods.

We address these challenges by introducing structure-aware learning approaches for clinical time-series classification and generation. Our main motivation is that existing models often treat multichannel physiological signals as generic data, without explicitly modeling their dynamical behavior, physical structure, or inter-lead relationships. To overcome this limitation, we explore several complementary directions: using Koopman-based temporal dynamics for self-supervised representation learning, incorporating ODE-based cardiac simulators into generative models, designing PDE-driven architectures that learn spatiotemporal representations of 12-lead ECGs, and integrating physiological priors into diffusion models for ECG generation.