Minjie Wang
Executive Director, Shanghai X-Lab at School of Computing and Data Science, The University of Hong Kong
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolAidyn Wang is an Associate Software Engineer based in New York with a B.S. in Computer Science and a minor in Cybersecurity from NYU, bringing 13 years of hands-on technical and practical experience to backend and full-stack development. At Hagerty he transitioned from a .NET engineering intern to an associate role, applying C#/.NET, SQL, and CI/CD practices while maintaining a focus on reliable, test-covered code. He has notable open-source contributions to high-profile machine learning projects like MXNet and DGL, where he implemented core threaded engines, graph neural network APIs, and performance optimizations in multi-threaded and CUDA contexts. Comfortable across networking, systems, and web stacks, Aidyn pairs deep algorithmic work with practical production delivery and a continuous learning mindset informed by early research and team-facing roles.
13 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at New York UniversityBachelor and Master's degree Computer Science, Bachelor and Master's degree Computer Science at Shanghai Jiao Tong University
Github Skills (20)
pytorch10
graph-algorithms10
c-language10
python10
multithreading10
testing10
machine-learning10
gt10
data-structure10
googletest10
deep-learning10
cuda10
data-structures10
configparser10
graph-neural-network10
Programming languages (6)
Github contributions (5)
dmlc/minerva
Mar 2014 - Jul 2015
Minerva: a fast and flexible tool for deep learning on multi-GPU. It provides ndarray programming interface, just like Numpy. Python bindings and C++ bindings are both available. The resulting code can be run on CPU or GPU. Multi-GPU support is very easy.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:461 commits, 5 PRs, 161 pushes in 1 year 4 months
Contributions summary:Minjie primarily focused on the development of features within the Minerva deep learning tool, with contributions centered around the core logic implementation. Their work included the development of a new class for a multi-threaded backend engine, and a range of classes in support, demonstrating an understanding of core algorithm and data structure implementation. Additionally, the user was involved in the improvement of multi-threaded data access, which indicates involvement in performance optimization.
pythonndarraygpu-supportpython-numpymulti-gpu
dmlc/dgl
Jun 2018 - Jan 2023
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:7 releases, 1192 reviews, 281 commits in 4 years 8 months
Contributions summary:Minjie's contributions primarily involved re-organizing the project folders, and implementing core APIs for deep learning on graphs, as evidenced by the code diffs in the graph.py file. They developed the basic APIs of graph neural networks, especially the message passing, and reduce functionality and improved efficiency with code reviews in the CUDA kernels. This indicates a focus on back-end development related to graph algorithms and their integration.
pytorchpythondeep-learningmachine-learninggraph-neural-networks
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