Yujia Zheng

Ningbo, Zhejiang, China

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Summary

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Yujia Zheng is a researcher-engineer with 8 years of experience who blends rigorous academic training—currently pursuing a PhD in Logic, Computation, and Methodology at Carnegie Mellon—with hands-on contributions to practical open-source tooling. Based in Ningbo, China, Yujia has a strong software engineering foundation (BS with a 3.96 GPA) and has held visiting/research stints at top institutions including UC Berkeley and National Taiwan University of Technology and Science. They contribute technical writing and user-facing documentation to influential Python causal discovery tooling (py-why/causal-learn), improving tutorials, benchmarks, and visualizations to make complex algorithms like PC, FCI, GES and LiNGAM accessible. Known for clear explanations and careful examples, Yujia bridges theory and practice—making advanced causal inference methods easier to adopt by researchers and engineers.

7 years of coding experience

Visiting student researcher, Computer Science, GPA 4.0/4.0, Visiting student researcher, Computer Science, GPA 4.0/4.0 at University of California, Berkeley

Exchange student, Computer Science, GPA 4.0/4.0, Exchange student, Computer Science, GPA 4.0/4.0 at National Taiwan University of Technology and Science

PhD, Logic, Computation, and Methodology, PhD, Logic, Computation, and Methodology at Carnegie Mellon University

Bachelor of Science - BS, Software Engineering, GPA 3.96/4.0, Bachelor of Science - BS, Software Engineering, GPA 3.96/4.0 at University of Electronic Science and Technology of China

Stackoverflow

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1reputation

0reached

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Github Skills (4)

documentation10

python8

causal-discovery8

sphinx7

Programming languages (6)

TypeScriptJavaShellHTMLJupyter NotebookPython

Github contributions (5)

py-why/causal-learn

Oct 2021 - Jan 2023

Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

Role in this project:

Technical Writer

Contributions:15 releases, 42 reviews, 168 commits in 1 year 2 months

Contributions summary:Yujia's contributions primarily involve updating documentation within the repository. These updates span across various aspects, including getting started guides, benchmarking results, and method-specific documentation for algorithms like PC, FCI, GES, LiNGAM, and others. The changes consistently focus on refining explanations, correcting errors, and adding code examples to enhance user understanding and clarify the functionality of the causal discovery methods. The user also updated the contributors list and enhanced the visualization for the results of algorithms like PC.

continouspythontranslationcausaltetrad

conda-forge/causal-learn-feedstock

Feb 2024 - Mar 2025

A conda-smithy repository for causal-learn.

Contributions:3 reviews, 13 PRs, 7 pushes in 1 year 1 month

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