Ruichu Cai
Professor at Guangdong University of Technology
Guangzhou City, Guangdong Province, China
Summary
🤩
Rockstar🎓
Top SchoolRuichu Cai is a professor of computer science at Guangdong University of Technology with six years of focused experience in machine learning, data mining, causality, and deep learning, building on a PhD jointly pursued with South China University of Technology and the National University of Singapore. He blends academic leadership and hands-on research, having progressed from lecturer to professor while collaborating with industry as a data mining consultant and researcher at ADSC. Ruichu contributes to open-source causal discovery tools—improving conditional independence tests, core PC/FAS algorithms, and time-series graph visualization in the well-regarded causal-learn Python library—bridging theoretical methods and practical implementations. His work is notable for improving kernel-based independence testing and BIC scoring in causal inference pipelines, reflecting a pragmatic strength in turning complex statistical ideas into usable software. Based in Guangzhou, he balances teaching, research, and open-source development to advance interpretable machine learning and causal analysis.
6 years of coding experience
4 years of employment as a software developer
Ph.D, Computer Science, Ph.D, Computer Science at South China University of TechnologyJoint Ph.D, Computer Science, Joint Ph.D, Computer Science at National University of Singapore
Github Skills (12)
algorithm10
data-structures10
statistics10
algorithms10
causal-discovery10
python10
data-structure10
causal-inference10
machine-learning9
graph-algorithms9
numpy8
pandas7
Programming languages (2)
Github contributions (4)
py-why/causal-learn
Jun 2021 - Dec 2021
Causal Discovery in Python. Learning causality from data.
Role in this project:
Data Scientist
Data ScientistContributions:12 commits, 14 pushes, 1 branch in 6 months
Contributions summary:Ruichu contributed to the `causal-learn` project by implementing and improving conditional independence tests and related algorithms. Their work involved modifying code for the Kernel-based Conditional Independence (KCI) test, including improvements to the BIC score calculation and fixing related bugs. The user also updated and enhanced the Fast Adjacency Search (FAS) and PC algorithms within the project, focusing on the core causal discovery methods. Furthermore, the user made improvements to graph visualization, including adding time-series graph visualization capabilities, and improved the documentation.
causal-discoverypythoncausalcausal-inferencecausality
ruichucai/ruichucai.github.io
Nov 2022 - Jun 2026
Contributions:124 pushes, 1 branch in 3 years 7 months
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