Clara-Mihaela Ionescu

Modelling, control & AI for biomedical systems

Our research focuses on modelling, control and AI for biomedical systems, with an emphasis on communication‑centric and control‑theoretic architectures that enable safe, adaptive and explainable clinical decision‑making. This work integrates system identification, physiological modelling and hybrid AI within a structured workflow spanning clinical expertise, patient-specific modelling, signal digitalization and synchronization, advanced control and optimization, and human-machine integration. The solutions provide a clinician-centered augmentation: reducing inter-patient variability, improving stability margins, and ensuring interpretable decision support under real-time constraints.

Complementary research addresses non-invasive monitoring and intelligent biomedical devices. This includes synergistic multi-signal analysis (e.g., lung-function testing), model-based design of devices operating in complex fluid–physiological environments, and clinical validation of monitoring technologies. Signal processing and synchronization layers transform heterogeneous biomedical data streams into structured state information, enabling integration with model-based control and AI inference.

Across all applications, the unifying scientific contribution is the development of a hierarchical human-in-the-loop control framework that couples mechanistic models with data-driven learning while maintaining stability, robustness and safety guarantees. The research thus positions biomedical decision-making as a modern control problem: uncertainty-aware, multi-scale, safety-critical and co-designed with clinical expertise.