Supervisor
Prof. Ruxandra Stoean
Her research interests include deep learning for image and signal processing in various real-world scenarios, with a particular attention on medical applications. Multi-modal and multi-organ data modelling is currently of special focus, in order to tailor powerful and effective AI models that see the full spectrum of a disease.
Selected publications
Cano-Domingo C. et al. | Deep learning event detector from long-term signal variation for seismic activity warning out of Schumann resonance. Knowl.-Based Syst. (2025), DOI: 10.1016/j.knosys.2025.114166
Cano-Domingo C. et al. | A hybrid deep learning approach for enhancing the Lorentzian curve fit algorithm for Schumann resonance. Expert Syst. Appl. (2025), DOI: 10.1016/j.eswa.2025.128681
Stoean R. et al. | Bridging the past and present: AI-driven 3D restoration of degraded artefacts for museum digital display. J. Cult. Herit. (2024), DOI: 10.1016/j.culher.2024.07.008
Ungureanu A. et al. | Learning deep architectures for the interpretation of first-trimester fetal echocardiography (LIFE): A study protocol for developing an automated intelligent decision support system for early fetal echocardiography. BMC Pregn. Childbirth (2023), DOI: 10.1186/s12884-022-05204-x
Research group website: Artificial intelligence and machine learning