John Pangas

Calgary, Alberta, Canada

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Summary

🤩

Rockstar

🎓

Top School

John Pangas is a software engineer and AI enthusiast with six years of experience and over three years focused on integrating LLMs and machine learning into practical software tools. Currently pursuing an M.S. in Electrical Engineering (Software Engineering specialization) at the University of Calgary, he has contributed to high-profile open-source projects like scikit-learn and Mozilla’s Bugbug, improving estimator robustness and automating bug triage. His Mozilla work includes building ML models to detect accessibility issues and spam on Bugzilla and a GPT-4–powered tool that automates generation and comparison of Firefox test cases, combining NLP with cosine-similarity evaluation. Comfortable bridging research and production, he has integrated model training with CI and experiment tracking (TaskCluster, Weights & Biases) to make ML workflows reproducible and interpretable. Fluent in both Western and Chinese academic contexts, he pairs strong academic performance with hands-on open-source impact and a stated goal of building AI tools that meaningfully improve people’s lives.

6 years of coding experience

Bachelor of Engineering - BE, Software Engineering, 4.13 / 5.0 (Average: 91%), Bachelor of Engineering - BE, Software Engineering, 4.13 / 5.0 (Average: 91%) at Zhejiang University of Technology

Master of Science - MS, Electrical Engineering (Software Engineering Specialization), Master of Science - MS, Electrical Engineering (Software Engineering Specialization) at University of Calgary

English, Bantu

Github Skills (9)

testing10

scikit10

machine-learning10

python10

data-science10

scikit-learn10

api-design9

data-analysis8

documentation8

Programming languages (3)

JavaJupyter NotebookPython

Github contributions (5)

scikit-learn/scikit-learn

Nov 2022 - Jan 2023

scikit-learn: machine learning in Python

Role in this project:

Data Scientist

Contributions:19 reviews, 5 commits, 10 PRs in 2 months

Contributions summary:John primarily contributes to the scikit-learn library by addressing issues related to estimator behavior and documentation. They fix potential `FutureWarning` messages within example code and implement tests to ensure error consistency for estimators. Additionally, the user enhances the library by raising `NotFittedError` exceptions in the `get_feature_names_out` method for specific estimators, improving the overall robustness and clarity of the API.

machine-learningpythonscikit-learnstatisticsdata-science

jpangas/scikit-learn

Nov 2022 - Mar 2025

scikit-learn: machine learning in Python

Contributions:72 pushes, 17 branches in 2 years 4 months

pythondata-sciencelearn-machine-learningmachine-learningscikit-learn

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