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About

Somayeh B. Shafiei is a pioneering researcher at the intersection of cognitive neuroscience, human-robot interaction, and surgical education, with a particular focus on robot-assisted surgery (RAS). Her work has fundamentally advanced our understanding of how cognitive and mental workload shape surgical performance, earning over 250 citations across her most influential publications. Shafiei's research employs cutting-edge neuroimaging tools — including electroencephalography (EEG), eye-gaze tracking, and functional brain network analysis — to objectively quantify surgeon expertise, mental workload, and skill acquisition in ways that traditional subjective assessments cannot capture. Her landmark 2015 paper on cognitive performance during RAS laid the conceptual groundwork for this emerging field, while subsequent studies explored mentor-trainee trust dynamics and the neuroscience of surgical mentorship. Notably, she has developed machine learning and deep neural network models capable of classifying surgical skill levels with meaningful precision, bridging neuroscience and artificial intelligence in clinical training contexts. Beyond technical innovation, Shafiei's contributions carry real-world significance: her frameworks offer potential pathways for standardizing surgical education, reducing trainee error, and improving patient safety. Her 2017 review on cognitive learning in urology further demonstrates her commitment to translating laboratory findings into practical surgical training reforms. She represents a compelling new generation of researcher redefining how we train tomorrow's surgeons.