Chen Gao
Research Scientist at Meta Reality Labs
Bellevue, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolChen Gao is a research scientist at Meta Reality Labs in Seattle with nine years of experience applying machine learning to video and vision problems. He blends research and engineering by adapting state-of-the-art optical flow models (including RAFT) and integrating non-local flow estimation into production-ready pipelines. His contributions to the ECCV 2020 Flow-edge Guided Video Completion project demonstrate a practical focus—implementing edge-guided completion, refining frame-loading logic, and improving pipeline robustness and efficiency. Chen’s work reflects both deep algorithmic understanding and careful attention to data and deployment details common in AR/VR research.
9 years of coding experience
6 years of employment as a software developer
Master’s Degree, Electrical and Computer Engineering, Master’s Degree, Electrical and Computer Engineering at University of MichiganDoctor of Philosophy - PhD, Computer Engineering, Doctor of Philosophy - PhD, Computer Engineering at Virginia TechBachelor’s Degree, Electrical and Computer Engineering, Computer Science, Bachelor’s Degree, Electrical and Computer Engineering, Computer Science at Oregon State University
Chinese, English
Github Skills (9)
video-processing10
pytorch10
computer-vision10
python10
optical-flow10
deep-learning8
machine-learning8
c-language4
cprogramming-language4
Programming languages (3)
Github contributions (5)
vt-vl-lab/FGVC
Sep 2020 - Dec 2021
[ECCV 2020] Flow-edge Guided Video Completion
Role in this project:
ML Engineer
ML EngineerContributions:12 commits, 10 pushes, 58 comments in 1 year 3 months
Contributions summary:Chen primarily contributed to the video completion project by implementing and refining the optical flow calculation and completion modules. Their work included adapting the RAFT model, integrating non-local flow estimation, and refining the overall video processing pipeline. Key changes involved adjusting the code for frame loading, ensuring only the first three channels were loaded, and integrating edge-guided completion techniques. These modifications indicate a focus on enhancing the accuracy and efficiency of the video completion process.
eccvcomputer-visionedgeeccv-2020video
vt-vl-lab/iCAN
Aug 2018 - Aug 2020
Contributions:9 commits, 6 pushes, 48 comments in 2 years
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