Eli Sheppard

Papers

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About

Eli Sheppard’s research lies at the critical intersection of human-robot interaction, developmental robotics, and assistive technologies for neurodiverse populations. Sheppard is best known for pioneering work in understanding and improving child-robot interaction, particularly for children with autism spectrum disorder (ASD). Their most cited paper (2018, 22 citations) investigates how typically developing children and those with ASD initiate joint attention with a gaze-contingent avatar—a core challenge given that 1 in 160 children globally has ASD and joint attention deficits are a hallmark of the condition. This work provides foundational insights for designing robots that can better engage and support children with social communication differences. Sheppard also contributes to multimodal representation learning for human-robot interaction (2020), developing neural network systems that ground the meaning of words in visual attributes like color, size, and object name, enabling more natural bidirectional communication between humans and machines. By combining rigorous experimental studies with cutting-edge AI, Sheppard’s research advances both the science of social robotics and the practical development of assistive technologies that can make a tangible difference in children’s lives.