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About

Jonas Gonzalez-Billandon is a researcher at the intersection of human-robot interaction (HRI), social robotics, and machine learning, whose work explores how robots can perceive, interpret, and respond to nuanced human behaviors. His most distinctive contributions lie in the domain of automated deception detection, where he has pioneered systems enabling humanoid robots to identify lies through multimodal cues — particularly gaze patterns — during natural social interactions. His 2019 paper on machine learning-based lie detection has garnered 29 citations, with follow-up studies reinforcing the viability of gaze-based approaches in realistic HRI contexts. Beyond deception, Gonzalez-Billandon has made meaningful advances in robot perception and cognition, developing biologically inspired architectures for joint attention, audiovisual face localization, and self-supervised speaker localization. His work on long-term personalization in robotic tutoring systems reflects a broader commitment to designing robots capable of sustained, adaptive relationships with human partners. More recently, his ROS-LLM framework signals a growing interest in embodied AI powered by large language models. With over 100 cumulative citations and contributions spanning cognitive architectures, interactive games, and social robot learning, Gonzalez-Billandon represents an emerging voice shaping how robots meaningfully participate in social and educational human environments.