Evi Zouganeli

Evi Zouganeli is an Associate Professor at the University of Oslo's Faculty of Mathematics and Natural Sciences, Department of Informatics. Her research spans from smart home technologies for elderly care to cognitive robotics and earlier work in optical networking. She leads interdisciplinary research in the "Assisted Living Project" funded by the Research Council of Norway under the SAMANSVAR programme (247620/O70), focusing on responsible innovations for dignified lives at home for people with mild cognitive impairment or dementia.

Her research interests center around smart home technology, activity recognition, and sensor data analysis for elderly care applications. She develops advanced machine learning approaches including probabilistic models like SPEED and Active LeZi, as well as deep learning techniques like LSTM networks for predicting sensor events and activities of daily living. Her work involves collecting data from real homes with older adult residents using binary sensors and depth video cameras, with applications in healthcare monitoring and assistive living technologies. More recently, she has expanded into cognitive robotics, investigating how cognitive architectures can enhance AI-enabled robotic systems.

Professor Zouganeli's publications show a clear research trajectory from optical networking in the early 2000s to her current focus on AI applications for healthcare. Her most recent work demonstrates strong interdisciplinary collaboration across computer science, healthcare, and gerontology fields. The research shows consistent improvement in prediction accuracy, with recent implementations achieving 77-87% accuracy for sensor event prediction and 61-90% for activity recognition depending on the apartment setup.

As a supervisor, she has guided PhD students in the Faculty of Mathematics and Natural Sciences at the University of Oslo, with research funded by the Research Council of Norway. Her work involves collaboration with researchers from multiple disciplines, as indicated by the interdisciplinary nature of the "Assisted Living Project." She has also contributed to educational research, particularly in project-based learning approaches for programming education in electrical engineering.