Gent Ymeri
Doctoral student

Presentation

My research focuses on building quantitative methods that turn data from everyday digital devices into reliable and useful health information. I work with mobile phones and wearables, using signal processing, statistical analysis, and machine learning to extract meaningful measures from real-world sensor data.

A central part of my work is methodological. I study how digital measures can be made technically sound, clinically relevant, and interpretable when collected outside controlled clinical settings. This includes developing end-to-end analysis pipelines from raw signals to validated metrics, and evaluating how these measures can support clinical assessment and long-term monitoring.

Alongside the more technical work, I study usability, user experience, and data interpretation, with a focus on how people engage with digital health tools and make sense of sensor-derived information in everyday life. While much of my applied work has been in neurological and chronic conditions such as Parkinson's disease, the underlying methods and design principles are broadly applicable across digital health contexts where objective, scalable, and data-driven assessment is needed.