Imperial College London

Dr Dandan Zhang

Faculty of EngineeringDepartment of Bioengineering

Lecturer in Artificial Intelligence & Machine Learning
 
 
 
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Contact

 

d.zhang17 Website

 
 
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Location

 

402Sir Michael Uren HubWhite City Campus

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Summary

 

Summary

Dr. Dandan Zhang is a Lecturer (US equivalent: Assistant Professor) in Medical Robotics in the Department of Bioengineering and a Lecturer in Artificial Intelligence and Machine Learning in the I-X initiative at Imperial College London. She is the Director of the Multi-Scale Embodied Intelligence Laboratory. She has been an honorary researcher in the Department of Electrical and Electronic Engineering at Imperial College London. Before joining Imperial, she was a Lecturer in the Department of Engineering Mathematics at the University of Bristol, affiliated with the Bristol Robotics Laboratory. She earned her Ph.D. degree from the Department of Computing and her MRes degree from the Department of Surgery and Cancer at Imperial College London. She has cross-disciplinary interests in robotics, biomedicine, and AI. Her current focus is on dexterous manipulation integrated with multi-modality sensor fusion and intelligence, and micro-robotics for biomedical engineering. She aims to enhance the level of autonomy for multi-scale robotic systems. The ultimate goal is to develop next-generation robots empowered by artificial general intelligence with super-human capabilities. She envisions that intelligent robots will reshape our world by providing tangible benefits in our daily life and contributing to healthcare systems.

Publications

Journals

Fan W, Guo X, Feng E, et al., 2023, Digital Twin-Driven Mixed Reality Framework for Immersive Teleoperation With Haptic Rendering, Ieee Robotics and Automation Letters, Vol:8, Pages:8494-8501

Lin Y, Church A, Yang M, et al., 2023, Bi-Touch: Bimanual Tactile Manipulation With Sim-to-Real Deep Reinforcement Learning, Ieee Robotics and Automation Letters, Vol:8, ISSN:2377-3766, Pages:5472-5479

Yang M, Lin Y, Church A, et al., 2023, Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing, Ieee Robotics and Automation Letters, Vol:8, ISSN:2377-3766, Pages:5480-5487

He Z, Zhang X, Jones S, et al., 2023, TacMMs: Tactile Mobile Manipulators for Warehouse Automation, Ieee Robotics and Automation Letters, Vol:8, ISSN:2377-3766, Pages:4729-4736

Zhang D, Gorochowski TE, Marucci L, et al., 2023, Advanced medical micro-robotics for early diagnosis and therapeutic interventions, Frontiers in Robotics and Ai, Vol:9, ISSN:2296-9144, Pages:1-19

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