Shuai Zhang
Senior Applied Scientist at Amazon Web Services (AWS)
California, United States
Summary
🤩
Rockstar🎓
Top SchoolShuai Zhang is a software engineer with nine years of experience building production systems and machine learning tooling across AWS, Meta, and earlier fintech and manufacturing startups. He combines cloud-scale engineering at AWS with hands-on ML model development—his open-source contributions include extending AutoGluon’s NER capabilities and adding recommender models (AutoRec, NeuMF, CML) to widely used repositories like DeepRec and d2l-en. Based in California, he holds an MS in Computer Science from Stevens Institute of Technology and has a track record of shipping robust data pipelines, model training workflows, and integration tests that bridge research code to production. Notably, he has experience integrating NER into PyTorch Lightning and improving recommendation performance through both new models and careful loss function design.
9 years of coding experience
7 years of employment as a software developer
Bachelor’s Degree, Information Management and Information System, Bachelor’s Degree, Information Management and Information System at Nanjing UniversityUNSW Sydney
English, Chinese
Github Skills (20)
transformers10
pytorch10
recommendation-system10
python10
data-science10
model-driven10
machine-learning10
model-building10
huggingface-transformers10
deep-learning10
tensorflow10
automl10
natural-language-processing10
recommender-system10
automated-machine-learning10
Programming languages (3)
Github contributions (5)
autogluon/autogluon
Oct 2022 - Jan 2023
Fast and Accurate ML in 3 Lines of Code
Role in this project:
ML Engineer
ML EngineerContributions:143 reviews, 20 commits, 56 PRs in 3 months
Contributions summary:Shuai primarily contributed to the implementation and enhancement of Named Entity Recognition (NER) features within the AutoGluon framework. Their commits involved adding NER functionalities, integrating dependent libraries like `seqeval`, fixing bugs related to data processing and configuration, and adding unit tests. The user also worked on integrating the NER module into the PyTorch Lightning framework, including loss functions and metrics.
forecastingimage-textmlppythonmeta-learning
d2l-ai/d2l-en
Jul 2019 - Aug 2022
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Role in this project:ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:37 reviews, 74 commits, 83 PRs in 3 years 1 month
Contributions summary:Shuai primarily contributed to the recommender systems chapter, adding matrix factorization and AutoRec implementations to the `d2l-en` repository. They introduced new functions for reading and splitting the MovieLens 100k dataset, and implemented training and evaluation methods for rating prediction. Further contributions included integrating ranking loss functions (BPRLoss and HingeLossbRec) and implementing the NeuMF model.
kagglepythontensorflowgaussian-processesnatural-language-processing
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