Zhiqiang Tang
Senior Applied Scientist at Amazon Web Services (AWS)
San Francisco Bay Area United States
Summary
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Rockstar🎓
Top SchoolZhiqiang Tang is a Senior Applied Scientist at AWS and the author and tech lead of AutoGluon Multimodal, an open-source toolkit that democratizes state-of-the-art multimodal deep learning with just three lines of code. With nine years of experience spanning industry research internships at IBM and AWS and a PhD background from Rutgers, he bridges rigorous academic research and production-grade ML engineering. He led the integration of PyTorch-based TextPredictor and experimental AutoMMPredictor into the widely used AutoGluon framework, contributing core functionality, tests, docs, and tutorials. Based in the San Francisco Bay Area, he focuses on making foundation models accessible and practical for real-world applications while maintaining a strong commitment to open-source collaboration.
9 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Rutgers University–New Brunswick
Github Skills (11)
pytorch10
machine-learning10
automated-machine-learning10
python10
natural-language-processing10
ensemble-learning9
testing9
computer-engineering9
scikit8
scikit-learn8
documentation8
Programming languages (2)
Github contributions (5)
autogluon/autogluon
Feb 2022 - Jan 2023
Fast and Accurate ML in 3 Lines of Code
Role in this project:
ML Engineer & Software Engineer
ML Engineer & Software EngineerContributions:1254 reviews, 107 commits, 329 PRs in 11 months
Contributions summary:Zhiqiang was actively involved in the development and release of the Pytorch-based TextPredictor and the experimental AutoMMPredictor, contributing significantly to the AutoGluon framework. Their work involved adding core functionality, including integrating Pytorch-based text prediction capabilities and new model components, and also encompassed addressing unit tests, documentation, and configuration details. Furthermore, the user made multiple contributions to the tabular tutorial, integrating the text predictor and adjusting code to accommodate for the changes.
forecastingimage-textmlppythonmeta-learning
zhiqiangdon/CU-Net
Jul 2018 - Aug 2021
Contributions:9 commits, 6 pushes, 5 comments in 3 years 1 month
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