Tongxin Bai
DevRel at NVIDIA
Haidian District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolTongxin Bai is a DevRel engineer at NVIDIA with 13 years of deep expertise in deep learning frameworks, compilers, and accelerated computing, bridging research and developer ecosystems. Previously he led a compiler group at BAAI where he helped create FlagGems, a Triton-based operator library now integrated with the PyTorch ecosystem, and contributed performance and correctness fixes to PaddlePaddle’s core (including a new Einsum API and autograd bug fixes). His background spans industry and academia—from reproducing AlphaFold2 and optimizer work at Baidu to research and edge-computing projects at Siemens and leading distributed systems research as an associate professor. With a PhD in computer science and early experience building high-performance distributed analytics, he combines deep algorithmic knowledge with practical system optimization and developer advocacy. Notably, he moves fluidly between low-level kernel tuning and developer-facing tooling, making complex acceleration tech accessible to practitioners.
13 years of coding experience
13 years of employment as a software developer
Bachelor of Science (BS) Mathematics, Bachelor of Science (BS) Mathematics at Fudan UniversityMaster's Degree Computer Science, Master's Degree Computer Science at Institute of Computing technology Chinese Academy of SciencesDoctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at University of Rochester
Github Skills (10)
paddlepaddle10
machine-learning10
deeplearning-ai10
deep-learning10
python10
numpy9
autograd9
distributed-training8
cprogramming-language7
c-language7
Programming languages (2)
Github contributions (5)
PaddlePaddle/Paddle
May 2021 - Mar 2022
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:42 reviews, 8 commits, 14 PRs in 10 months
Contributions summary:Tongxin's contributions primarily focused on refactoring and enhancing the performance of the `dot` operation's CPU kernel within the PaddlePaddle framework. They introduced a new Einsum API, including implementation, test coverage, and documentation improvements. Further commits involved fixing bugs and correcting output dimension errors in the Einsum functionality. Moreover, the user contributed to the autograd module by fixing bugs on handling v=None in vjp and jvp, and adding tests.
pytorchpythonparalleldeep-learningpaddlepaddle
tongxin/Paddle
Apr 2021 - Apr 2022
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）
Contributions:159 pushes, 8 branches in 1 year
pytorchparalleldeep-learningreinforcement-learningindustrial
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