Public defence: Pedro Maria Rego Lencastre E Silva
Pedro Maria Rego Lencastre E Silva will defend his thesis “Reflecting on the eyes: New & old models for intelligent health applications” for the degree of PhD in Engineering Science.
Pedro Maria Rego Lencastre E Silva will defend his thesis “Reflecting on the eyes: New & old models for intelligent health applications” for the degree of PhD in Engineering Science.
The candidate will defend his thesis on 10. September 2026 at 14:30.
The trial lecture and the public defence will be streamed (oslomet.zoom.us).
Webinar ID: 659 3666 2774
Passcode: 2026
Kazi Shah Nawaz Ripon, Associate Professor, Faculty of Technology, Art and Design, Department of Computer Science, OsloMet
Norun Christine Sanderson, Associate Professor, Department of Computer Science, OsloMet
Eye-tracking measures where we look and how our pupils change, giving a quick, non-invasive window into attention, perception, and mental effort. Because it’s becoming easier to do, inside VR headsets and even with ordinary cameras, it has real potential as an early screening tool for conditions like autism and ADHD, where earlier support can improve long-term outcomes.
But research on eye movements is oddly fragmented. Physicists often model gaze as a simple kind of random motion; clinicians use standardized tests and split behaviour into two phases: fixations (when we take in information) and saccades (fast jumps between points); and AI can learn directly from complex data, but the result is often a “black box” that’s hard to trust or interpret in healthcare.
In this thesis, we connect these approaches and show that simple, interpretable models that switch between a few states can reproduce key patterns of gaze better than the generative AI models we tested.
Building on that, we show how these models, combined with standard machine-learning classifiers, can efficiently detect ADHD-related gaze signatures. We also present evidence that gaze is best described by models that explicitly represent switching between phases, and we report new findings suggesting
Overall, we aim to give a more unified picture of how gaze behaves, while making it more useful for practical, trustworthy applications in intelligent health.
