Nelson Liu
PhD Candidate
Tulsa, Oklahoma, United States
Summary
🤩
Rockstar🎓
Top SchoolNelson Liu is a PhD candidate and software engineer with 11 years of experience building and maintaining production-quality ML and NLP tooling. Based in Tulsa, he contributes to high-profile open-source projects such as AllenNLP, torchtext, and scikit-learn, focusing on backend development, test automation, and DevOps for robust model/data pipelines. His work includes integrating advanced models (ELMo, Biattentive Classification Network), improving library test coverage, and setting up CI/CD—demonstrating a pragmatic blend of research-minded modeling and engineering discipline. Nelson’s contributions to widely used repos show a knack for refactoring, documentation, and reproducible pipelines that help downstream researchers and engineers. Less obvious is his cross-cutting focus on maintainability—typo fixes, PEP8 alignment, and doc tweaks that materially raise long-term project quality. He pairs academic depth from Stanford NLP circles with hands-on software craftsmanship across the ML stack.
12 years of coding experience
Bachelor of Science - BS, Economics, 1st Year, Bachelor of Science - BS, Economics, 1st Year at University of PennsylvaniaHolland Hall SchoolBachelor of Science - BS, Economics, 1st Year, Bachelor of Science - BS, Economics, 1st Year at The Wharton School
Stackoverflow
46reputation
843reached
1answer
2questions
Github Skills (44)
pytorch10
continuous-integration10
python10
data-science10
model-driven10
scikit10
testing10
machine-learning10
cin10
model-building10
deep-learning10
natural-language-processing10
scikit-learn10
bayesian-network10
nlp10
Programming languages (18)
Github contributions (5)
allenai/allennlp
Jul 2017 - Apr 2021
An open-source NLP research library, built on PyTorch.
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:4 reviews, 97 commits, 188 PRs in 3 years 9 months
Contributions summary:Nelson made significant contributions to the AllenNLP library, focusing on improving the existing code and adding new features related to natural language processing. Their work includes enhancing existing components, such as adding string representations to token classes, fixing test cases, and adding new dataset readers, specifically for the Stanford Sentiment Treebank and Conll2000. They also integrated and implemented the Biattentive Classification Network and addressed issues related to ELMo integration.
natural-language-processingpytorchnlpdeep-learningdata-science
scikit-learn/scikit-learn
Nov 2015 - Jun 2017
scikit-learn: machine learning in Python
Role in this project:Data Scientist
Data ScientistContributions:55 commits, 59 PRs, 1091 comments in 1 year 6 months
Contributions summary:Nelson primarily contributed to the scikit-learn project by improving existing examples and addressing inaccuracies. Their commits involved updating code to use alternative functions and fix deprecation warnings related to external libraries like `matplotlib` and `scipy`. They also made documentation improvements by editing the FAQ and adding a link to the user guide. These changes focused on ensuring the examples and documentation are up-to-date and accurate, highlighting their role in maintaining the quality of the library's educational resources.
machine-learningpythonscikit-learnstatisticsdata-science
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