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Daniel Rakita is a Professor at Yale Engineering, specializing in robotics and computer science. His research focuses on motion planning and optimization algorithms, aiming to generate effective and feasible robot manipulator motions in real-time. By developing core motion algorithms, he works on creating shared-control interfaces that are intuitive for novice users, enabling them to control robotic manipulation platforms in various complex scenarios such as remote homecare, telenursing, disaster relief, and more. His interdisciplinary work integrates concepts from machine learning and human-robot interaction to create generalizable solutions. Rakita has received several accolades for his contributions to the field, including the Outstanding Graduate-Student Research Award from UW-Madison and the Cisco Graduate Student Fellowship. His publications include significant works presented at top conferences in the robotics domain, showcasing advancements in path optimization and teleoperation.
Administered via the Graduate School of Arts and Sciences (GSAS). GRE General is optional for PhD.