Sarah Dean
Papers
1
Total Citations
4
H-Index
1
About
Sarah Dean is a leading researcher at the intersection of robotics, machine learning, and human-robot interaction, with a focus on developing safe, adaptive, and assistive systems. Her core contributions center on human-in-the-loop learning algorithms, particularly contextual bandits, which enable robots to intelligently query human feedback when faced with uncertainty. In her highly cited work, "To Ask or not to Ask: Human-in-the-loop Contextual Bandits with Applications in Robot-Assisted Feeding," Dean addresses a critical challenge in assistive robotics: how robots can autonomously acquire diverse food items while knowing when to seek caregiver input. This work, with 4 citations, exemplifies her broader impact in creating algorithms that balance autonomy and human guidance. Dean’s research has profound implications for caregiving technologies, enhancing the quality of life for individuals with motor impairments. Her achievements include advancing the theoretical foundations of safe exploration in robotics and demonstrating practical applications in real-world assistive systems. Through her innovative blend of theory and application, Dean is shaping a future where robots can collaborate seamlessly with humans, ensuring both efficiency and safety in critical caregiving tasks.
Research Focus
Key Achievements
Top Papers
- 1