Guolin Ke
Senior Vice President Of Artificial Intelligence at DP Technology
Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolGuolin Ke is a senior AI leader and machine learning researcher with a decade of experience building high-performance models and production systems, now serving as Senior Vice President of Artificial Intelligence at DP Technology. He specializes in AI for Science—large-scale representation learning for molecules, 3D geometry, molecular generation and protein structure prediction—and has led open-source projects that bridge research and practice. Guolin created LightGBM during his MSRA internship, a widely used GBDT library with ~14K GitHub stars and 220M+ downloads, and later led Graphormer and Uni-Fold efforts that won top prizes in OGB KDD Cup and NeurIPS Open Catalyst and reimplemented AlphaFold with public training code. His background combines deep research at Microsoft Research Asia with hands-on systems work (distributed parameter servers, Allreduce optimizations) and real-world wins in algorithm competitions. Based in Beijing, he focuses on accelerating scientific discovery via scalable machine learning and actively recruits collaborators who want to push AI-driven science into production.
10 years of coding experience
9 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at Xiamen University
Stackoverflow
11reputation
1kreached
1answer
0questions
Github Skills (25)
pytorch10
c-language10
python10
pre-trained-model10
pretrain10
mpi10
machine-learning10
distributed-systems10
data-structure10
deep-learning10
data-structures10
cprogramming-language10
modeling10
network-programming9
zeromq9
Programming languages (18)
Github contributions (5)
deepmodeling/Uni-Mol
May 2022 - Oct 2022
Official Repository for the Uni-Mol Series Methods
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 38 reviews, 26 commits in 5 months
Contributions summary:Guolin primarily contributed to the repository by releasing code, tutorials, and examples. Their commits include an initial code release, adding environment setup and pretraining tutorials, and adding molecular property prediction tutorials. They also made several updates to the README.md, requirements.txt, and Dockerfile, indicating involvement in the project's setup and documentation. Code changes show contributions to the core model file, specifically, related to the UniMol architecture.
pytorchrepresentationpre-trained-modelcheminformaticsproteins
microsoft/LightGBM
Aug 2016 - Mar 2021
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:41 releases, 485 reviews, 867 commits in 4 years 8 months
Contributions summary:Guolin contributed to fixing warnings and issues, primarily in the C++ code base of the LightGBM library. They addressed code discrepancies and warnings related to signed and unsigned integers, and also fixed issues related to the handling of missing values in the dataset. They also updated and improved code for both model saving and data handling tasks.
kagglepythondata-mininglightgbmmicrosoft
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