Ziming Liu
Research Intern at Microsoft
Singapore, Singapore
Summary
🤩
Rockstar🎓
Top SchoolZiming Liu is a PhD candidate at NUS's HPC-AI Lab and a research intern at Microsoft Research Asia, specializing in machine learning systems and high-performance computing with eight years of industry and research experience. He has worked on pipeline parallelism for deep learning and is currently exploring long-sequence training for large language models, bridging systems research with practical scalability challenges. His contributions to the ColossalAI project show hands-on work in distributed training internals and CUDA kernel refinements, focusing on robustness and code quality in large-model tooling. With a background from Peking University and NUS, plus prior roles at ByteDance and HPC-AI Tech, he blends NLP engineering experience with deep systems expertise and is actively seeking collaborations and internships.
8 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Peking University博士学位, Computer Science, 博士学位, Computer Science at 新加坡国立大学Master's degree, Artificial Intelligence, Master's degree, Artificial Intelligence at National University of Singapore
Github Skills (6)
pytorch10
deep-learning10
python10
distributed-computing10
cuda9
ai8
Programming languages (1)
Github contributions (5)
hpcaitech/ColossalAI
Mar 2022 - Jan 2023
Making large AI models cheaper, faster and more accessible
Role in this project:
ML Engineer
ML EngineerContributions:6 reviews, 9 commits, 27 PRs in 10 months
Contributions summary:Ziming's commits primarily focus on formatting and code style improvements within the ColossalAI library, specifically related to process group initialization and other internal components. They made changes to files related to distributed training, including updates to 2.5D, 2D, 3D, and 1D initializer files. Additionally, the user's work demonstrates a focus on improving code style within the CUDA kernel, including modification of embedding bag functionality.
heterogeneous-trainingcolossal-aifinetuningdeep-learninginference
MaruyamaAya/ColossalAI_llama
Jun 2023 - Jul 2023
Making large AI models cheaper, faster and more accessible
Contributions:13 PRs, 111 pushes in 1 month
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