Darryl Lam

Papers

Total Citations

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About

Darryl Lam is a rising researcher at the intersection of human-robot interaction (HRI) and affective computing, with a focus on endowing machines with social intelligence. His most cited work, “Empathetic Robots Using Empathy Classifiers in HRI Settings” (2025), pioneers the integration of multi-modal empathy classifiers with empathetic text generation, enabling robots to perceive and respond to human emotional states in real time. By designing a system that combines visual, auditory, and textual cues, Lam’s research addresses a critical gap in HRI: the ability for robots to not only detect but also appropriately express empathy, fostering deeper rapport between humans and machines. Though early in his career, his work has already garnered attention, with 3 citations reflecting its novelty and potential impact. Lam’s contributions are particularly notable for their practical application—his demo showcases a tangible step toward socially aware robots capable of nuanced emotional interaction. As the field moves toward more natural human-robot collaboration, Lam’s research on empathetic AI stands as a foundational effort, promising to shape future developments in assistive robotics, mental health support, and everyday human-machine coexistence.