Hongxuan Li
Graduate Research Assistant
Raleigh-Durham-Chapel Hill Area United States
Summary
🤩
Rockstar🎓
Top School
Top expert inAWS Cloud Computing and Machine Learning Development
Hongxuan Li is an applied scientist and MSc-level AI engineer with seven years of experience building and deploying machine learning systems across academia and industry, currently interning at Amazon and pursuing research roles at UNC/NC State. He combines foundation-model training expertise with MLOps know-how—contributing to high-profile AWS projects like SageMaker examples and Deep Learning Containers where he fixed numerical stability in LDA notebooks and maintained TensorFlow container builds and tests. His background spans protein engineering, medical imaging for cross-site Alzheimer’s diagnosis, and AI education initiatives, showing a talent for translating research into robust, production-ready artifacts. Comfortable in Docker-, TensorFlow-, and SageMaker-driven pipelines, he brings a pragmatic focus on numerical correctness and reproducible ML stacks. Notably, he has moved fluidly between motion planning, biomedical applications, and foundation-model training, reflecting a rare mix of domain breadth and systems-level engineering.
6 years of coding experience
5 years of employment as a software developer
Master of Engineering - MEng Artificial Intelligence, Master of Engineering - MEng Artificial Intelligence at Duke UniversityBachelor's degree Computer Science, Bachelor's degree Computer Science at Hebei University of Technology
Github Skills (16)
machine-learning10
topic-modeling10
docker10
mlops10
tensorflow10
aws10
python10
dockers10
numpy10
sagemaker10
kubernetes-pods9
deep-learning9
kubernetes9
gunicorn8
data-analysis8
Programming languages (5)
Github contributions (5)
aws/amazon-sagemaker-examples
Oct 2020 - Jul 2021
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:169 reviews, 340 commits, 540 PRs in 9 months
Contributions summary:Hongxuan's contributions primarily focused on addressing probability out-of-bound issues within the LDA topic modeling notebooks, specifically modifying Python scripts and the model logic to ensure numerical stability and valid probability distributions. They also reverted a previous commit, likely a syntax fix or cleaning, to restore the intended functionality within the training notebook. This suggests involvement in model debugging and ensuring numerical stability in the AWS SageMaker environment.
pythonjupyter-notebooktrainingawssagemaker
aws/deep-learning-containers
Sep 2021 - Mar 2022
AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.
Role in this project:MLOps Engineer
MLOps EngineerContributions:39 reviews, 16 commits, 21 PRs in 6 months
Contributions summary:Hongxuan primarily focused on adapting and maintaining the AWS Deep Learning Containers, particularly for TensorFlow. Their contributions included adding and updating Dockerfiles for different TensorFlow versions (2.6, 2.7, 2.8) and GPU/CPU configurations, adjusting build specifications to streamline image creation, and integrating the TensorFlow Serving API. They also updated tests, including Sagemaker integration tests, to ensure the compatibility of the containers.
pytorchsagemakercontainersmxnetserving
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