Yuang Liu
Brookline, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolYuang Liu is a software engineer and MS Computer Science candidate at Boston University with six years of coding experience across Java, Python, and C and a strong foundation in object-oriented design. He has delivered measurable product improvements in industry internships—boosting blog recommendation engagement by combining TF-IDF, RNNs, CNN spam filters, and later replacing Bi-LSTM with BERT for a further uplift—and has shipped substantial codebases in academic projects. On open-source, he contributed performance-focused enhancements to the PaddlePaddle core and inference demos, adding mixed-precision and TensorRT GPU support to accelerate model inference. Comfortable working in teams of varied sizes, he pairs practical engineering with machine learning expertise to turn research models into production-ready systems.
6 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at 美国波士顿大学学士, Computer Science, 3.5 （4.0）, 学士, Computer Science, 3.5 （4.0） at 北京邮电大学
English
Github Skills (10)
tensorrt10
paddlepaddle10
machine-learning10
c-language10
deep-learning10
cprogramming-language10
performance-optimization10
python10
neural-network9
distributed-training8
Programming languages (4)
Github contributions (5)
PaddlePaddle/Paddle
Aug 2022 - Jan 2023
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1187 reviews, 46 commits, 696 PRs in 5 months
Contributions summary:Yuang primarily contributed to the PaddlePaddle framework, focusing on enhancements related to TensorRT integration and mixed-precision support. Their work involved improving the TensorRT engine's data type consistency, including fixing bugs. The user also made modifications to support FP16 in fused embedding, eltwise layernorm, and skip_layernorm operations, showcasing contributions related to performance optimization. Additionally, they addressed issues with the TensorRT engine context memory and fixed bugs related to the model's shape.
pytorchpythonparalleldeep-learningpaddlepaddle
PaddlePaddle/Paddle-Inference-Demo
Aug 2022 - Jan 2023
Role in this project:ML Engineer
ML EngineerContributions:8 reviews, 25 commits, 104 PRs in 4 months
Contributions summary:Yuang primarily contributed to optimizing and extending the paddle-inference-demo repository, focusing on GPU inference enhancements. They modified existing Python and C++ code to enable mixed-precision inference, particularly GPU fp16 support. Additional commits involved integrating TensorRT, including relevant documentation updates, demonstrating a focus on performance improvements for NVIDIA GPU inference.
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.