Eunsu Kang

Papers

Total Citations

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About

Eunsu Kang is a pioneering researcher at the intersection of artificial intelligence, robotics, and the arts, whose work redefines creative expression through technology. Her primary research areas include robotic painting, human-robot collaboration, and machine learning for artistic brushstroke generation. Kang’s major contribution lies in developing a machine learning approach that enables robots to learn and replicate human brushstroke techniques, bridging the gap between computational precision and artistic style. Her most-cited paper, "Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists" (2020, 23 citations), addresses a long-standing challenge in robotic art by moving beyond simple mechanical reproduction to capture the nuanced, expressive qualities of human painting. This work has significant implications for both the arts and robotics, offering new models for human-robot creative collaboration. Kang’s interdisciplinary achievements have positioned her as a leading voice in the emerging field of AI-driven art, inspiring students and researchers to explore how machines can augment—rather than replace—human creativity. Her research continues to push the boundaries of what is possible when art and technology converge.