Ignavier Ng
Pittsburgh, Pennsylvania, United States
Summary
🤩
Rockstar🎓
Top SchoolIgnavier Ng is a researcher-engineer with nine years of experience at the intersection of machine learning, causality, and large-scale AI systems, currently pursuing a PhD in Logic, Computation, and Methodology at Carnegie Mellon. He has interned across top research labs including Microsoft Research (Redmond and Asia), Mila, Huawei Noah’s Ark Lab, X, and AWS, contributing to productivity and foundational AI projects. His open-source testing work on the causal-learn Python library demonstrates a rigorous emphasis on correctness and reproducibility—adding unit tests for A* and DP search algorithms and improving test infrastructure. Fluent in both research and engineering, he bridges theory and practice by turning algorithmic ideas into verifiable code and reliable experiments. Based in Pittsburgh, he combines strong academic training (University of Toronto MS, HKUST BS) with hands-on experience in industry-scale research environments. A not-obvious asset: he focuses on testability and benchmark validation, a skill often underrepresented in research codebases but critical for production-grade ML tools.
10 years of coding experience
MS in Applied Computing, MS in Applied Computing at University of TorontoPhD in Logic, Computation, and Methodology, PhD in Logic, Computation, and Methodology at Carnegie Mellon UniversityHong Kong University of Science and Technology (HKUST)
Malay, Chinese, English, Chinese
Github Skills (8)
unit-testing10
causal-discovery10
python10
pytest9
causal-inference9
test-framework8
testing8
algorithms7
Programming languages (3)
Github contributions (5)
py-why/causal-learn
Jul 2022 - Jul 2022
Causal Discovery in Python. Learning causality from data.
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:1 review, 8 commits, 2 PRs in 2 days
Contributions summary:Ignavier primarily focused on writing and maintaining unit tests for the `causal-learn` repository. They added tests for the Astar and DP search algorithms, ensuring the correctness of the implementations by comparing results against benchmark data. The user also updated the test infrastructure by modifying data paths and fixing documentation, while refactoring existing test cases to improve maintainability.
causal-discoverypythoncausalcausal-inferencecausality
ignavierng/notears-tensorflow
Sep 2019 - Sep 2020
Reimplementation of NOTEARS in Tensorflow
Contributions:21 commits, 2 PRs, 13 pushes in 1 year
tensorflow
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