Shi Yongliang is an outstanding candidate for the Best Researcher Award. His research not only demonstrates innovation and technical mastery but also addresses real-world challenges in robotics and AI. His contributions to large-scale systems, combined with a consistent record of high-impact publications, make him highly suitable for this award.
Education
Shi Yongliang holds a Ph.D. in Bionics and Robotics from the Beijing Institute of Technology, completed in 2021. His doctoral studies focused on developing advanced robotic systems and AI integration. Prior to that, he earned an M.Sc. in Precision Instruments and Machinery from North University of China in 2016, where he gained hands-on experience in machine design, instrumentation, and control systems. Shi began his academic journey with a Bachelor of Engineering in Vehicles Engineering from the same institution in 2013. His educational background has been instrumental in shaping his research in AI and robotics, providing a strong foundation in mechanical engineering, automation, and intelligent systems. Shi’s multidisciplinary education allows him to approach his research from a holistic perspective, integrating hardware and software solutions for robotics and autonomous systems.
Experience
Shi Yongliang’s professional journey began with his role as a Postdoctoral Researcher at Tsinghua University’s Department of Computer Science and Technology, where he worked from October 2021 to December 2023. During this time, he contributed to several groundbreaking projects in the fields of robotics, navigation, and AI. Shi’s expertise spans the areas of 3D/4D reconstruction, semantic mapping, and global localization in large-scale environments. Before joining Tsinghua, he completed his Ph.D. at the Beijing Institute of Technology, focusing on robotics and AI integration. His early career also includes earning his Master’s degree in Precision Instruments and Machinery and Bachelor’s in Vehicles Engineering from North University of China, laying the groundwork for his advanced research. Shi’s work consistently pushes the frontiers of AI and robotics, making him a key contributor to the development of future intelligent systems.
Awards and Honors
Shi Yongliang has been recognized with several prestigious awards for his contributions to robotics and AI. As a Postdoctoral Researcher at Tsinghua University, he received the “Best Paper Award” at the IEEE International Conference on Robotics and Automation (ICRA) in 2023 for his groundbreaking work on robotic global localization. During his Ph.D., he earned the “Excellence in Research Award” from the Beijing Institute of Technology for his innovative research on neural semantic mapping. Shi was also awarded the “Outstanding Graduate Researcher” title at North University of China during his Master’s studies. His achievements highlight his dedication to advancing autonomous systems and his impactful contributions to the fields of embodied AI, 3D/4D reconstruction, and smart city applications. Shi’s consistent performance in research and development has earned him a reputation as an emerging leader in AI and robotics.
Research Focus
Shi Yongliang’s research focuses on three major areas: Navigation, Embodied AI, and 3D/4D Reconstruction. His work aims to address the challenges of robotic navigation in complex environments, with a particular emphasis on city-scale neural semantic mapping. Shi has developed innovative methods for robotic localization and mapping, applying AI to improve the accuracy and efficiency of autonomous systems. His research on 3D/4D reconstruction leverages AI to create dynamic, real-time representations of environments, which are essential for autonomous navigation. Shi is also actively exploring Embodied AI, which integrates physical systems with AI to enable robots to perform tasks in real-world environments more effectively. His work has significant implications for the development of autonomous vehicles, smart cities, and intelligent navigation systems, pushing the boundaries of AI-driven robotics.
Publication Top notes
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Mars: An instance-aware, modular and realistic simulator for autonomous driving
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Latitude: Robotic global localization with truncated dynamic low-pass filter in city-scale nerf
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Design of a hybrid indoor location system based on multi-sensor fusion for robot navigation
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Robotic binding of rebar based on active perception and planning
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LCPF: A particle filter lidar SLAM system with loop detection and correction