Predicting Human Performance Through Pupillometry

Contributed to developing a foundational model of pupillometry to predict human performance under varying cognitive and physical demands. By analyzing changes in pupil diameter, which are linked to cognitive load and task performance, the aim was to create predictive ML models that can monitor performance in real-time. This model will be applicable across a wide range of high-stress environments, from space missions to other domains requiring cognitive and physical endurance.