Dott.ssa Alessia Rondinella is a Postdoctoral Researcher at the MIFT Department of the University of Messina, as part of the research project “AI-Powered Digital Twins for Neurodegeneration Research: Advancing Disease Progression Modeling (NeuroTwin),” dedicated to the development of artificial intelligence methodologies for modeling the progression of neurodegenerative diseases through Digital Twin-based approaches. In 2026, she was a Postdoctoral Researcher at the Department of Mathematics and Computer Science of the University of Catania, as part of the project “Integration of Artificial Intelligence into Brain Image Analysis in Patients with Multiple Sclerosis.”

Since 2024, she has been a Research Fellow at the Department of Mathematics and Computer Science of the University of Catania, as part of the project “HiCONNECTS – Heterogeneous Integration for Connectivity and Sustainability.”

She obtained a Ph.D. in Artificial Intelligence as part of the 37th cycle of the National Ph.D. Program in Artificial Intelligence – Health and Life Sciences area, organized by the University Campus Bio-Medico of Rome and the University of Catania, conducting research in the field of Medical Imaging.

In 2021, she obtained a Master’s Degree in Computer Engineering with honors (110/110 cum laude) from the Department of Electrical, Electronic and Computer Engineering of the University of Catania. In 2018, she obtained a Bachelor’s Degree in Computer Engineering from the same Department.

Since 2021, she has been a member of the IPLab research group at the University of Catania. She attended the International Computer Vision Summer School (ICVSS) in 2022 and, from 2022 to 2026, was a member of the organizing committee of the International Forensics Summer School (IFOSS). She also contributes to the scientific community by serving as a reviewer for international journals and conferences.

Her main research interests concern Deep Learning applied to Medical Imaging. In particular, her research has focused on the analysis of brain magnetic resonance images in patients with multiple sclerosis, with particular emphasis on lesion segmentation, as well as on the study of neurodegenerative diseases through brain atrophy and brain age estimation methodologies. She has also worked on CT image analysis tasks, with applications to the detection of infections in patients with hip prostheses and the detection of COVID-19 in chest CT scans. She is the author of articles published in international journals and several contributions presented at international scientific conferences.