Tianwei Yin
Research Scientist at Reve Image
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTianwei Yin is a research scientist based in Palo Alto with eight years of experience building and deploying machine learning systems, currently focused on text-to-image generation at Reve Image. He holds a PhD in Computer Science from MIT and a BS in Mathematics and Computer Science from UT Austin, combining deep academic rigor with practical engineering. Previously a research intern at Adobe, Tianwei blends research-level modeling work with hands-on backend and DevOps skills, evidenced by multi-GPU bug fixes and inference pipeline improvements in open-source projects like CenterPoint. His contributions emphasize reliability and production readiness—fixing evaluation and licensing issues and streamlining data loading for stable deployments. Colleagues would note his knack for translating experimental models into reproducible, deployable systems and for catching subtle correctness problems that surface only at scale. He also maintains a public preview of his text-to-image work, signaling an interest in accessible demos and user-facing research impact.
8 years of coding experience
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Massachusetts Institute of TechnologyBachelor of Science - BS Mathematics and Computer Science, Bachelor of Science - BS Mathematics and Computer Science at The University of Texas at AustinThe Affiliated High School of South China Normal University
Github Skills (7)
python10
debug9
debugging9
pytorch9
deep-learning8
continuous-deployment8
ml-deployment8
Programming languages (3)
Github contributions (5)
tianweiy/CenterPoint
Jun 2020 - Oct 2022
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:3 releases, 6 reviews, 103 commits in 2 years 4 months
Contributions summary:Tianwei primarily focused on bug fixes, with the most significant contribution being a fix for a multi-GPU evaluation bug. They also corrected licensing information and made small improvements to the data loading process, demonstrating a focus on code stability and project correctness. Additionally, the user made some changes to the inference scripts, indicating a role in optimizing the model deployment pipeline.
tianweiy/MVP
Nov 2021 - Oct 2022
Contributions:14 commits, 10 pushes, 60 comments in 11 months
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