xyupeng
PhD Candidate
Summary
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RockstarPhD candidate in computer science with four years of software engineering experience, contributing to high-performance and AI-focused projects from an HPC-AI lab at the National University of Singapore. Practical experience as an ML engineer on the well-known Colossal-AI project, where contributions improved code quality, pipeline parallelism modules, and CUDA extension build setup—showing both systems-level and GPU-aware development skills. Combines research rigor with pragmatic engineering: comfortable refactoring complex codebases, polishing tests, and smoothing build processes for GPU-accelerated components. Interest in making large AI models cheaper and more accessible, with hands-on familiarity in production-oriented ML tooling and parallelism strategies.
4 years of coding experience
Github Skills (9)
cuda10
pytorch10
deep-learning10
python10
ai10
parallelization9
data-parallelism8
data-parallel8
distributed-computing8
Programming languages (2)
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:7 commits, 8 PRs in 10 months
Contributions summary:xyupeng primarily contributed to code style improvements and format fixes within the Colossal-AI project. Their work included refactoring and polishing code in various modules related to pipeline parallelism, builder configurations, and testing of sequence layers. They also addressed formatting issues in the CUDA extension setup, demonstrating an understanding of the build process for GPU-accelerated components.
heterogeneous-trainingcolossal-aifinetuningdeep-learninginference
xyupeng/BETA
May 2022 - Jan 2023
Contributions:7 commits, 8 pushes, 1 branch in 8 months
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