Yotam Sechayk

Papers

Total Citations

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About

Yotam Sechayk is a researcher at the forefront of affective computing and human-robot interaction (HRI), with a focus on enhancing machines’ ability to interpret human emotional states through non-verbal cues. His primary research areas include 3D Morphable Models (3DMMs), data augmentation techniques, and arousal-valence prediction—a framework for mapping emotional dimensions of intensity and positivity. Sechayk’s major contribution lies in developing innovative data augmentation strategies for 3DMM-based systems, enabling more robust prediction of arousal and valence from facial expressions and body gestures. This work is critical for creating socially aware robots that can respond naturally to human emotions, bridging the gap between computational models and real-world HRI scenarios. His most cited paper, “Data Augmentation for 3DMM-based Arousal-Valence Prediction for HRI” (2024), has garnered 1 citation, reflecting its emerging impact in a rapidly evolving field. By addressing the challenge of limited training data in affective computing, Sechayk’s research paves the way for more adaptive and empathetic robotic systems, making him a promising voice in the intersection of computer vision, machine learning, and social robotics.