Xuan Yi Li
About Xuan Yi Li
Xuan Yi Li is a Machine Learning Engineer currently employed at DJI in Shenzhen, Guangdong, China. With a background in Computer Science and Electrical Engineering, Li has experience in machine learning research and collaboration on 3D perception projects.
Current Role as Machine Learning Engineer at DJI
Xuan Yi Li currently holds the position of Machine Learning Engineer at DJI, a role they have occupied since 2021. Based in Shenzhen, Guangdong, China, Li focuses on applying machine learning techniques to enhance the company's product offerings. Their work involves developing algorithms and models that improve the performance of DJI's technology.
Previous Experience at DJI and ByteDance
Prior to their current role, Xuan Yi Li worked at DJI as a Machine Learning Researcher in 2020 for five months. During this time, they contributed to various research projects. Additionally, Li briefly worked at ByteDance in 2021 for one month as a Machine Learning Researcher in Beijing, China, where they engaged in research activities related to machine learning.
Educational Background in Computer Science
Xuan Yi Li earned a Master's degree in Computer Science from Nankai University, where they studied from 2018 to 2021. This academic background provided them with a solid foundation in computer science principles and machine learning methodologies. Prior to this, Li completed a Bachelor's degree in Electrical and Electronics Engineering at Xidian University from 2014 to 2018.
Research Collaboration and Projects
Xuan Yi Li collaborates with Dr. Xiaozhi Chen on projects that focus on 3D perception, specifically in the areas of 3D object detection and stereo matching. This collaboration highlights Li's commitment to advancing research in machine learning and computer vision.
Open Source Contributions on GitHub
Li maintains a GitHub profile under the username 'meteorshowers', where they likely share and contribute to open-source projects. This platform allows them to engage with the broader tech community and showcase their work in machine learning and related fields.