Wenliang Gao is a Machine Learning Engineer with a decade of experience building search, NLP, and ML systems across Meta, Amazon, and Rakuten Institute of Technology. He focuses on ML planning and efficiency, with a research background in sentiment analysis, data mining, and long-standing interests in NLP dating to his PhD work at The University of Tokyo. At Amazon he worked on query understanding, spam detection and content filtering, and at Meta he applies that expertise to production-scale ML problems. He also brings systems-level skills—evidenced by an open-source ROS package for IMU analysis that implements Allan variance tooling—reflecting practical experience in robotics and flight systems from time at DJI. Based in Shenzhen with degrees in software engineering and informatics, he combines rigorous research foundations with hands-on engineering for production ML.
10 years of coding experience
4 years of employment as a software developer
University of Tokyo
Bachelor (secondary), Japanese language, Bachelor (secondary), Japanese language at 大连理工大学
A ROS package tool to analyze the IMU performance.
Role in this project:
Backend Developer
Contributions:32 commits, 1 PR, 18 pushes in 6 months
Contributions summary:Wenliang primarily focused on developing a ROS package for IMU data analysis. Their contributions involved the implementation of an Allan variance analysis, including the creation of data structures and functions for processing and analyzing IMU data. The user added code to calculate Allan variance, deviation, and other related metrics. They also integrated the package with ROS, creating nodes to subscribe to IMU data and output the analysis results.
Contributions:42 commits, 10 PRs, 34 pushes in 28 days
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