Farzan Sasangohar
Papers
2
Total Citations
29
H-Index
2
About
Farzan Sasangohar is a leading researcher at the intersection of human factors engineering, healthcare systems, and cognitive ergonomics. His work focuses on improving safety and performance in high-stakes medical environments, particularly through the integration of physiological sensing and machine learning. A major contribution is his pioneering use of electroencephalogram (EEG) and eye-tracking data to objectively assess surgical skill. In a highly cited 2023 study (22 citations), he developed machine learning models that classify surgical expertise levels in robot-assisted surgery by analyzing neural and visual attention patterns. He further advanced this line of inquiry in 2024 by creating models to evaluate performance and learning rates, moving beyond subjective, rater-dependent assessments. By demonstrating that cognitive workload and gaze behavior can predict surgical proficiency, Sasangohar’s work lays the foundation for real-time feedback systems and automated training tools in surgery. His research has significant implications for reducing medical errors and accelerating skill acquisition, establishing him as a key figure in the future of data-driven, human-centered healthcare.
Research Focus
Key Achievements
Top Papers
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