Xin Liu
Software Engineer at Google
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolXin Liu is a software engineer with nine years of experience building large-scale distributed backend systems at Google and AWS, specializing in Java, Python, Ruby, and cloud-native AWS services like DynamoDB, Kinesis, SQS, and CloudFormation. He combines strong academic foundations (MS in ECE, 3.96 GPA) with practical operational excellence, consistently introducing tools and processes that improve service performance and reliability. At AWS he contributed to IoT Analytics infrastructure, and at Google he works on foundational models for health and sensor data, blending research sensibilities with production-grade engineering. An active open-source contributor, he improved efficiency and preprocessing/postprocessing pipelines for a NeurIPS 2023 rPPG toolbox, showing skill in both backend optimization and data-driven model tooling. Based in Seattle, he brings a pragmatic, metrics-driven approach to architecting resilient systems that bridge research and large-scale production.
9 years of coding experience
Bachelor of Engineering (B.Eng.), Electronic Information Engineering, Bachelor of Engineering (B.Eng.), Electronic Information Engineering at University of Electronic Science and TechnologyMaster of Science (MS), Electrical and Computer Engineering, 3.96/4.0, Master of Science (MS), Electrical and Computer Engineering, 3.96/4.0 at The Ohio State University
English, Chinese
Github Skills (8)
computer-vision10
deep-learning10
preprocessing10
performance-optimization10
python10
preprocess10
machine-learning9
algorithms9
Programming languages (2)
Github contributions (5)
ubicomplab/rPPG-Toolbox
Jul 2021 - Jan 2023
rPPG-Toolbox: Deep Remote PPG Toolbox (NeurIPS 2023)
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:93 reviews, 37 commits, 74 PRs in 1 year 6 months
Contributions summary:Xin primarily focused on improving the efficiency and functionality of the rPPG-Toolbox, a deep learning toolbox for remote physiological measurement. Their contributions included adding time per frame calculations, fixing bugs in the preprocessing pipeline, and tweaking efficiency tests. Furthermore, the user added postprocessing capabilities for the processed data. This indicates involvement in improving the core functionality of the toolbox and optimization of its algorithms for processing.
cameracardiovacomputer-visiondeep-learninghealth
xliucs/MTTS-CAN
Oct 2020 - Sep 2022
Contributions:29 commits, 20 pushes, 1 branch in 2 years
pytorchmulti-taskshiftneurips-2020on-device
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