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portfolio

RelEYEance: Real-Time Gaze-Based AI Reliance Detection

This project introduces the RelEYEance model for detecting user reliance on AI in real-time. Leveraging eye-tracking data, we developed a clustering pipeline to classify user reliance into over-reliance, under-reliance, and appropriate reliance during a time-sensitive monitoring task. The model enables adaptive interventions for recalibrating reliance.

Highlight-Informed Saliency Model (HISM) for GUI Design

This project explores how dynamic visual highlights influence user attention in graphical user interfaces (GUIs). Using eye-tracking data, we analyzed temporal changes in gaze behavior across free-viewing and task-driven scenarios. Our novel Highlight-Informed Saliency Model (HISM) integrates spatial and temporal data to predict attention shifts over time, outperforming existing models. These insights enable the design of more effective and user-focused GUIs.

Influence of Work Motivation and Task Difficulty on Human Reliability.

Published in Proceedings of the 2nd International Conference on Reliability Systems Engineering (ICRSE 2017), Beijing, 2017, 2017

Understanding and Predicting Temporal Visual Attention Influenced by Dynamic Highlights in Monitoring Task

IEEE Transactions on Human-Machine Systems (Under Review), 2025

RelEYEance: Gaze-based assessment of users’ AI-reliance at run-time

Published in ACM Symposium on Eye Tracking Research and Application (ETRA 2025), 2025

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.

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