Zangwei Zheng
Tech Lead at HPC-AI Tech
Singapore
Summary
🤩
Rockstar🎓
Top SchoolZangwei Zheng is a tech lead and Ph.D. candidate at the National University of Singapore with eight years of experience building AI-driven products and research systems. He led pretraining for the video generation model Open-Sora and directed the product development and launch of Video Ocean, a next-generation AI video creation platform at HPC-AI Tech. His background blends academic rigor—research internships at UC Berkeley and a Distinguished Paper Award–recognized project at ByteDance—with hands-on engineering delivering measurable improvements to recommendation and CTR systems. An active contributor to large-scale AI tooling, he has refined code quality in the ColossalAI project, focusing on communication, kernel, and memory-management modules for distributed deep learning. Based in Singapore, he combines full-stack development instincts with deep learning research, often smoothing the bridge from prototype to production.
8 years of coding experience
1 year of employment as a software developer
High School Diploma, High School Diploma at Jiangsu Xishan Senior High School 江苏省锡山高级中学Bachlor, Computer Science and Technology, 4.61/5.00, Bachlor, Computer Science and Technology, 4.61/5.00 at Nanjing UniversityPh.D., Computer Science, Ph.D., Computer Science at National University of Singapore
Chinese, English, Japanese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (6)
pytorch10
deep-learning10
distributed-computing10
python9
ai8
hpc6
Programming languages (7)
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, 9 PRs in 10 months
Contributions summary:Zangwei primarily focused on code style improvements and refactoring within the ColossalAI project. Their contributions involved polishing code in modules related to communication, kernel operations, and Gemini memory management. The changes primarily focused on adjusting code formatting and style, suggesting a focus on maintaining code quality within the distributed deep learning framework.
heterogeneous-trainingcolossal-aifinetuningdeep-learninginference
zhengzangw/NJUOS2018-zzw
Mar 2019 - Aug 2019
Contributions:82 commits, 1 push, 1 branch in 5 months
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