Qiwei Ye
Technical Head at Beijing Academy of Artificial Intelligence
Haidian District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolQiwei Ye is a technical head and principal research investigator at BAAI’s Research Center for Computational Biology with 11 years of experience bridging machine learning research and production systems. Previously a senior researcher at Microsoft Research Asia, he co-led or contributed to high-impact projects such as LightGBM and the Suphx Mahjong AI—one of the strongest Mahjong systems on Tenhou—and has deep expertise in generative models, decision trees, and deep reinforcement learning. He combines systems-level contributions (distributed ML frameworks and core C++/header improvements) with applied research in gaming, robotics, bioinformatics, and music, now focusing on advancing fundamental life-science problems with AI. Known for improving code quality and developer experience in high-performance open-source projects, he pairs rigorous academic training from Peking University with a knack for turning complex research into robust, real-world tools.
11 years of coding experience
6 years of employment as a software developer
Master's degree, Master's degree at Peking University
English, Chinese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (11)
decision-tree10
mpi10
code-library10
c-language10
lib10
cprogramming-language10
lightgbm10
gradient-boosting10
distributed-systems9
gbm9
documentation8
Programming languages (4)
Github contributions (5)
microsoft/Multiverso
Nov 2015 - Jun 2017
Parameter server framework for distributed machine learning
Role in this project:
Back-end Developer
Back-end DeveloperContributions:203 commits, 20 PRs, 74 pushes in 1 year 7 months
Contributions summary:Qiwei primarily focused on fixing macro issues and improving the codebase by modifying several header files related to the `multiverso` library. The changes include adjusting macros in core files like `parameter_loader.h`, `double_buffer.h`, `mt_queue.h`, and others. They also contributed to merging branches and correcting irregularities in the usage of MPI, indicating involvement in core library functionality.
parameter-servermachine-learningparameterserver-frameworkdistributed-machine-learning
microsoft/LightGBM
Oct 2016 - Oct 2018
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Role in this project:Back-end Developer
Back-end DeveloperContributions:1 review, 60 commits, 114 PRs in 2 years
Contributions summary:Qiwei primarily contributed to the code documentation by updating and clarifying comments within the codebase. Their changes focused on the header files related to the LightGBM bin and application modules. These modifications likely aimed to improve code readability and maintainability, demonstrating a focus on code quality and developer experience within the project.
kagglepythondata-mininglightgbmmicrosoft
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