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About

Dakota Sullivan is an emerging researcher whose work sits at the intersection of human-robot interaction, privacy, and collaborative robotics. Sullivan's most influential contribution, "Confidant: A Privacy Controller for Social Robots" (2022, 18 citations), tackles the underexplored challenge of equipping social robots with the ability to recognize and appropriately manage sensitive conversational information — a critical concern as robots become embedded in everyday domestic and professional environments. Building on this foundation, Sullivan's broader research agenda consistently prioritizes human-centered design, exploring how robots can be integrated into spaces responsibly and effectively. In the manufacturing domain, Sullivan has examined how collaborative robots (cobots) can be deployed in ways that genuinely respect both business objectives and worker preferences, demonstrating a commitment to sociotechnical balance. Complementary work on the "Lively" motion framework addresses how robots can move expressively and contextually in social scenarios, while research into interactive product packaging reveals an imaginative approach to enriching first-contact human-robot experiences. Most recently, Sullivan has formalized a privacy-by-design framework addressing data collection, retention, and exposure risks in robotic systems. Across these contributions, Sullivan demonstrates a thoughtful, multidisciplinary perspective that positions privacy and human welfare as foundational principles in robotics research.