Anne Reissing is a highly accomplished Research Scientist at Google DeepMind, specializing in the intersection of robotics, machine learning, and human-robot interaction. She earned her PhD in Computer Science from the Karlsruhe Institute of Technology (KIT), where her research focused on intuitive robot programming and human-robot collaboration. Following her PhD, she was a postdoctoral researcher at ETH Zurich, further honing her expertise in robotic learning and control. At Google DeepMind, Anne contributes to cutting-edge research aimed at developing more capable, adaptable, and safe robotic systems. Her work often involves reinforcement learning, imitation learning, and creating robots that can understand and respond to complex environments and human guidance. She is passionate about building intelligent machines that can seamlessly assist humans in a variety of tasks.
Anne Reissing's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Authored and co-authored numerous influential research papers published in top-tier robotics and AI conferences and journals (e.g., CoRL, ICRA, RSS, NeurIPS), advancing the fields of robot learning from demonstrations, reinforcement learning for manipulation, and safe human-robot interaction.
Successfully completed her doctoral studies at the Karlsruhe Institute of Technology (KIT) with a dissertation focusing on innovative approaches to make robot programming more intuitive and facilitate closer collaboration between humans and robots in shared workspaces.
Conducted impactful postdoctoral research at ETH Zurich, one of the world's leading universities for technology and engineering, further developing advanced algorithms for robotic perception, learning, and control in complex, dynamic environments.
Plays a key role in Google DeepMind's efforts to push the boundaries of AI and robotics, contributing to projects that aim to imbue robots with greater intelligence, dexterity, and the ability to learn complex tasks more efficiently.
The University of Alabama
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