Kun Gao
Researcher at Zhongguancun Academy
Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolKun Gao is a research scientist based in Beijing with a PhD from Peking University and five years of experience at the intersection of explainable AI, symbolic reasoning, and applied time-series analysis. His work spans inducive logic programming, injecting symbolic rules into deep models, and interpretable solutions for finance, healthcare, and industrial cloud logs—grounded in practical internships at Microsoft, IHPC, and Japan’s National Institute of Informatics. Comfortable with TensorFlow, PyTorch, cloud platforms and classical ML techniques, he bridges theory and engineering to make black-box models auditable and rule-aware. Notably, his research emphasizes differentiable rule learning from real-world knowledge graphs and temporal data, reflecting a rare blend of symbolic and deep learning expertise focused on interpretable, deployable systems.
5 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Peking UniversityBachelor's degree Information Security , Bachelor's degree Information Security at University of Science and Technology Beijing
Chinese, English
Github Skills (26)
ranking10
react9
science9
computer-science9
python7
deep-learning6
machine-learning6
pytorch6
lightning6
artificial-intelligence6
lifecycle6
ai5
data-science5
infrastructure4
scaling4
Programming languages (2)
Github contributions (5)
Awesome3DGS/3D-Gaussian-Splatting-Papers
Dec 2023 - Apr 2025
3D高斯论文，持续更新，欢迎交流讨论。
Contributions:1 review, 6 PRs, 492 pushes in 1 year 3 months
3d-gaussian-splatting3dgsgaussian-splattingnovel-view-synthesis
gqk/LAE
Mar 2023 - Oct 2024
Contributions:10 pushes, 5 branches, 8 comments in 1 year 7 months
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