Yue Zhao
Los Angeles, California, United States
Summary
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RockstarYue Zhao is an Assistant Professor of Computer Science at USC and a CMU-trained PhD with about a decade of experience focused on anomaly/outlier/OOD detection, ML systems, and AutoML. They bridge rigorous research and production engineering—contributing core functionality and test coverage to well-known anomaly-detection libraries like pyod and pygod, and building data pipelines and experiment tooling for healthcare deep learning (PyHealth). Their background includes research internships at Microsoft, NortonLifeLock and IQVIA and consulting analytics experience at PwC, giving them practical fluency in deploying ML in enterprise and healthcare settings. Notably, Yue often improves reproducibility and usability in projects (docstring cleanup, test coverage, downloader tooling), reflecting a hands-on commitment to making research systems reliable and production-ready.
10 years of coding experience
Stackoverflow
51reputation
3kreached
5answers
0questions
Github Skills (37)
pytorch10
documentations10
health10
preprocessing10
anomaly-detection10
python10
data-science10
preprocess10
testing10
data-mining10
pandas10
machine-learning10
data-preprocessing10
deep-learning10
e-health10
Programming languages (5)
Github contributions (5)
yzhao062/pyod
Oct 2017 - Dec 2022
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:39 releases, 1193 commits, 257 PRs in 5 years 3 months
Contributions summary:Yue's contributions primarily focused on improving the core functionality of the outlier detection library pyod, specifically by addressing errors in the test setup, increasing the test coverage, and improving the documentation for consistency and completeness. They also implemented the integration of the DeepSVDD and the new Deep Generative Models. Further updates include refactoring existing code to increase readibility and for addressing issues of code coverage, including testing the edge cases.
pythondata-miningoutlierspython2outlier-ensembles
yzhao062/anomaly-detection-resources
May 2018 - Aug 2022
Anomaly detection related books, papers, videos, and toolboxes
Role in this project:Data Scientist & Back-end Developer
Data Scientist & Back-end DeveloperContributions:229 commits, 21 PRs, 229 pushes in 4 years 4 months
Contributions summary:Yue contributed to the development of a file downloader script, which suggests involvement in data acquisition and resource management. The script, written in Python, downloads and renames files from URLs, demonstrating back-end development skills. Further commits show the user working on a URL checker and link replacement within the documentation, indicating an effort to maintain data integrity and potentially improve the user experience. The contributions align with the project's focus on anomaly detection resources, particularly with a "paper downloader" script.
anomalytime-series-analysispythonoutlier-detectionunsupervised-learning
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