Tianqi Xu
Software Engineer at Preferred Networks, Inc.
Chiyoda, Japan
Summary
🤩
Rockstar🎓
Top SchoolTianqi Xu is a software engineer with 11 years of experience specializing in backend systems and machine learning infrastructure, currently working at Preferred Networks in Tokyo. He holds a PhD from Tokyo Institute of Technology and has a strong research-to-production track record spanning academia, national labs, and industry. His open-source contributions include improvements to prominent ML projects such as Optuna (hyperparameter optimization), PFRL (deep reinforcement learning), and Chainer, where he focused on robustness, multiprocessing safety, and numeric edge cases like NaN handling and float16 support. Comfortable with both low-level communicator and storage backend work, he blends rigorous academic training with pragmatic engineering to harden training pipelines and CLI tooling. An often-overlooked strength is his attention to documentation and UX in developer tools—cleaning help messages and descriptions to reduce friction for other contributors.
11 years of coding experience
5 years of employment as a software developer
Master's degree, Dept. of Mathematical and Computing Sciences, Master's degree, Dept. of Mathematical and Computing Sciences at 東京工業大学Doctor of Philosophy - PhD, Dept. of Mathematical and Computing Sciences, Doctor of Philosophy - PhD, Dept. of Mathematical and Computing Sciences at Tokyo Institute of Technology
Github Skills (21)
pytorch10
python10
chainer10
command-line-interface10
machine-learning10
reinforcement-learning10
numpy10
deeplearning-ai10
deep-learning10
gpu10
command-line10
multiprocessing10
cli10
batch-normalization9
mpi9
Programming languages (2)
Github contributions (5)
optuna/optuna
Jul 2020 - Nov 2022
A hyperparameter optimization framework
Role in this project:
Back-end Developer
Back-end DeveloperContributions:37 commits, 11 PRs, 18 comments in 2 years 3 months
Contributions summary:Tianqi primarily contributed to improving the `optuna/optuna` codebase by refining the help messages and descriptions within the command-line interface (CLI). They addressed grammatical errors and corrected documentation. Furthermore, the user focused on refining the code for reporting and intermediate values within the storage backends, especially for the RDB storage, incorporating the ability to handle `NaN` values correctly.
pythonoptimization-frameworkparallelhyperparameteroptimization
chainer/chainer
Mar 2019 - Dec 2019
A flexible framework of neural networks for deep learning
Role in this project:Backend Engineer
Backend EngineerContributions:130 commits, 46 PRs, 18 pushes in 9 months
Contributions summary:Tianqi's commits primarily focus on enhancing the `chainermn/chainer` repository by adding support for single-node communicators, including the implementation of features like allreduce_grad and bcast. They also introduced support for float16 data types within the chainermn communicators, such as flat, hierarchical, and non-cuda-aware communicators, and added relevant tests. Furthermore, the user refactored the batch normalization implementation and made related fixes and code style improvements.
cudapythonmxnetcaffe2flexible-framework
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