Yan Ni
Senior Software Engineer at Pony.ai
Haidian District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolYan Ni is a Senior Software Engineer with 11 years of experience building robust planning and control systems, currently focusing on autonomous driving at Pony.ai in Beijing. Previously at Microsoft, he worked on cloud and tooling projects and contributed to high-profile open-source AutoML efforts (notably improving tuners and GPU checks in microsoft/nni). He holds a B.S. and an M.S. from Peking University in computer science and computer system architecture, grounding his applied work in strong systems knowledge. Yan blends practical production engineering with ML tooling improvements, often surfacing subtle compatibility and resource-management fixes that improve developer and runtime experience.
11 years of coding experience
3 years of employment as a software developer
Bachelor of Science (B.S.), Computer Science, 3.52, Bachelor of Science (B.S.), Computer Science, 3.52 at 北京大学
Chinese
Github Skills (8)
hyperparameter-tuning10
machine-learning10
automated-machine-learning10
python10
neural-architecture-search9
pytorch9
deep-learning8
deeplearning-ai8
Programming languages (6)
Github contributions (5)
microsoft/nni
Oct 2018 - Jun 2020
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 174 commits, 153 PRs in 1 year 8 months
Contributions summary:Yan's contributions primarily revolve around modifying and refactoring the NNI tuner's code, specifically the reward extraction logic within various tuner implementations like `MetisTuner`, `HyperoptTuner`, and `EvolutionTuner`. They have updated and standardized how trial results and metrics are handled, enhancing compatibility across different tuner classes. Furthermore, the user added crucial checks for GPU resources in the training service's local and remote machine modes. These changes suggest an effort to optimize the functionality of the AutoML toolkit and enhance the user experience.
pythonneural-architecture-searchengineeringtensorflowbayesian-optimization
leckie-chn/nni
Oct 2018 - May 2020
An open source AutoML toolkit for neural architecture search and hyper-parameter tuning.
Contributions:2 PRs, 162 pushes, 67 branches in 1 year 7 months
pythonneural-architecture-searchtensorflowhyperparameter-tuning
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