Fei (Felix) Lu
About Fei (Felix) Lu
Fei (Felix) Lu is a Machine Learning and Robotics Research Engineer with expertise in computer vision and autonomous driving. He holds a Bachelor's degree in Naval Architecture and Ocean Engineering from Shanghai Jiao Tong University and a Master's degree in Mechanical Engineering in Robotics and Control from Carnegie Mellon University.
Work at OffWorld
Fei Lu has been employed at OffWorld since 2021, serving as a Machine Learning and Robotics Research Engineer. In this role, he focuses on developing and implementing machine learning solutions tailored for autonomous systems. His work contributes to the company's initiatives in robotics and artificial intelligence, particularly in enhancing the capabilities of autonomous technologies.
Education and Expertise
Fei Lu holds a Bachelor's degree in Naval Architecture and Ocean Engineering from Shanghai Jiao Tong University, which he completed from 2014 to 2018. He furthered his education at Carnegie Mellon University, earning a Master's degree in Mechanical Engineering with a focus on Robotics and Control between 2018 and 2020. His academic background provides a solid foundation for his expertise in reinforcement learning and machine learning applications in robotics.
Background
Before joining OffWorld, Fei Lu worked at Shanghai Waigaoqiao Shipbuilding Co., Ltd. as a Ship Exterior Designer for one month in 2017. This position allowed him to gain practical experience in design and engineering within the maritime industry. His transition to robotics and machine learning reflects a shift in focus towards advanced technologies and their applications in autonomous systems.
Achievements in Robotics
Fei Lu has made significant contributions to the field of robotics, particularly in the development of reinforcement learning algorithms for real-world applications. His work aims to advance the integration of machine learning in robotic systems, enhancing their functionality and efficiency in various environments. He actively seeks opportunities to further explore computer vision and autonomous driving technologies.