Gabriella Olmo

Gabriella Olmo is a Full Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where she also serves as a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab. Her academic and research activities are centered on biomedical signal processing, neuroscience, and artificial intelligence, with a strong emphasis on healthcare applications. She is actively involved in doctoral education, supervising PhD candidates in Computer and Systems Engineering, Artificial Intelligence, and Biomedical Engineering, and has contributed to doctoral programs at both the Polytechnic University of Turin and the University of Turin.

Her research interests include biomedical signal processing, neuroscience, artificial intelligence, telecommunications, and digital health, with applications in Parkinson’s disease, sleep disorders, neonatal monitoring, and stress assessment. She leads several major research initiatives funded by national and European programs, including PNRR, EU Horizon Europe, and private foundations, focusing on wearable technologies, AI-driven diagnostics, and telemedicine.

Her recent publications demonstrate a consistent trend in developing AI-powered, non-invasive solutions for monitoring neurodegenerative and sleep disorders using wearable sensors and signal processing. These works span topics such as freezing of gait detection, REM sleep behavior disorder, neonatal pain assessment, and deep brain stimulation effects, reflecting a strong interdisciplinary focus on clinical translation of engineering innovations.

She has led or managed numerous competitive research projects, including OMNIA-PARK, PERSIMMON, S-CoDe, and NODES, and holds patents in distributed arithmetic coding and video storage. Her leadership roles as Scientific Director and Contractual Manager highlight her active engagement in both academic and applied research.

She advises a large cohort of PhD students working on AI in healthcare, computer vision, and biomedical signal analysis. Her teaching portfolio includes courses in Signal Analysis, Telemedicine Technologies, Artificial Intelligence in Medicine, and Machine Learning in Healthcare, across undergraduate, master's, and doctoral levels.

She is a key member of the SMILIES research group, which focuses on resilient computing architectures for life sciences, and collaborates with healthcare institutions such as the Orfanelle Nursing Home on geriatric frailty monitoring.