Scholar

LIU Zhe

Google Scholar ID: ld6KI1UAAAAJ

University of Cambridge, Shanghai Jiao Tong University

Multi-robot systemRobot controlTask planningLaser SLAMSelf-driving

Citations & Impact

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Citations

3,269

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Publications

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Publications

20 items

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Resume (English only)

Academic Achievements

He has published more than 70 papers in refereed professional journals and international conference proceedings, such as T-PAMI, T-RO, T-MECH, T-ITS, T-CST, T-ASE, T-IP, T-CYB, CVPR, ICCV, ICRA, IROS. He has won the best conference paper awards of IEEE ROBIO2023, IEEE ROBIO2022, IEEE CYBER 2022, and IEEE RCAR 2020, the honorable mention for the best paper award of IEEE RAL in 2019, the best conference paper finalist award of IEEE RCAR 2017, and the 2nd place of robot benchmarking demonstration league of RoboCup 2015. He serves as an Editor of ICRA 2025~2027, an Associate Editor of IEEE Transactions on Automation Science and Engineering 2024~2026 and IROS 2025, and received the Outstanding Associate Editor Award of ICRA 2023. He was a recipient of National Outstanding Young Talents Programme (Overseas) in 2022 and Shanghai Outstanding Young Talents Programme (Overseas) in 2022.

Research Experience

Currently, Dr. Zhe LIU is a tenure-track associate professor of the Department of Automation, Shanghai Jiao Tong University. He has also worked as a Postdoctoral Fellow at the Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, and as a Research Associate at the Department of Computer Science and Technology, University of Cambridge.

Education

Received the Ph.D. degree from Shanghai Jiao Tong University in 2016. From 2017 to 2020, he was a Postdoctoral Fellow at Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong. From 2020 to 2022, he was a Research Associate of Department of Computer Science and Technology, University of Cambridge, United Kingdom.

Background

Research interests include scheduling and optimization of large-scale robotic systems, resilient navigation of industrial self-driving systems, localization of self-driving systems, coordination of multi-robot formations, and robot control.

Miscellany