Ran Cheng
About Ran Cheng
Ran Cheng is a Senior Staff Research Engineer at Midea, specializing in LiDAR semantic segmentation and scene completion algorithms. With a background in robotics and software engineering, he has held various research and engineering roles at institutions such as McGill University, Huawei, and UCLA.
Work at Midea Group
Ran Cheng serves as a Senior Staff Research Engineer at Midea Group since 2021. His role is based in Shanghai, China, where he focuses on advanced robotics research within Lab 2030@Midea-Robozone. His work involves developing algorithms for 3D robot perception, SLAM, 3D Object Detection, Panoptic Segmentation, and Multi-Object Tracking (MOT). Cheng's expertise in LiDAR semantic segmentation and scene completion algorithms supports the division's mission to innovate in robotics technology.
Previous Experience at McGill University
Before joining Midea, Ran Cheng worked at McGill University as a Research Assistant in the Mobile Robotics Lab from 2017 to 2020. During his three years in Montreal, Canada, he contributed to various projects related to mobile robotics, enhancing his skills in algorithm development and research methodologies.
Experience at Huawei
Ran Cheng was employed at Huawei as a Research Engineer from 2019 to 2021 in Markham, Ontario, Canada. His two-year tenure involved working on advanced technology projects, where he applied his engineering skills to develop innovative solutions in the field of telecommunications.
Educational Background
Ran Cheng holds a Master's degree in Computer Science from McGill University, which he completed from 2017 to 2019. He also earned a Bachelor's degree in Software Engineering from Tongji University, where he studied from 2011 to 2015. His academic background laid a strong foundation for his expertise in robotics and algorithm development.
Research and Development Skills
Ran Cheng specializes in LiDAR semantic segmentation and scene completion algorithms. His current research interests include 3D robot perception, SLAM, 3D Object Detection, Panoptic Segmentation, and Multi-Object Tracking (MOT). These skills are critical in advancing the capabilities of robotics and enhancing machine perception in various applications.