Srinjoy Majumdar

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About

Srinjoy Majumdar is a leading researcher at the intersection of computer vision, human-robot interaction, and autonomous systems. His work focuses on enabling robots to understand and predict human behavior through visual cues, particularly gaze following—a critical capability for natural, fluent human-robot collaboration. In his highly cited 2018 paper "Human Gaze Following for Human-Robot Interaction" (56 citations), Majumdar proposed a novel approach that allows robots to predict human gaze fixations, thereby inferring a person's intentions and engagement levels during interaction. This work has significant implications for assistive robotics, manufacturing, and collaborative AI systems. Majumdar also contributes to robotic perception and safety, as demonstrated in his 2017 work on "Viewpoint selection for visual failure detection" (8 citations), which addresses the challenge of detecting subtle task failures—like a screw tip near versus inside a hole—by intelligently selecting optimal camera viewpoints. His research bridges the gap between low-level visual processing and high-level task understanding, making robots more perceptive, safe, and intuitive partners in shared environments.