Lu Lu
Assistant Professor Of Statistics And Data Science
United States
Summary
🤩
Rockstar🎓
Top SchoolLu Lu is a research scientist with 11 years of experience focused on scientific machine learning and physics-informed models. As an active back-end developer and ML engineer, they have substantially refactored and extended the popular DeepXDE library—adding modular FNN components, MIONet support, multiple activation and sampling methods, and both TensorFlow and PyTorch backends. Their work bridges research and production, enabling reproducible model saving/restoring and practical examples that accelerate adoption in scientific computing. Listed as an Assistant Professor of Statistics and Data Science on GitHub, Lu combines academic rigor with hands-on engineering to translate advanced methods into usable open-source tooling. A detail that stands out: they favor modular, backend-agnostic designs that make complex physics-informed networks easier to experiment with and deploy.
11 years of coding experience
2 years of employment as a software developer
M.Sc., Engineering, M.Sc., Engineering at Brown UniversityMinor, Computer Technology and Application, Minor, Computer Technology and Application at Tsinghua University
Stackoverflow
73reputation
4kreached
0answers
2questions
Github Skills (11)
neural-network10
pytorch10
automatic-differentiation10
machine-learning10
tensorflow10
python10
back-end-development10
latex6
gnuplot6
cairo6
jax5
Programming languages (5)
Github contributions (5)
lululxvi/deepxde
Feb 2019 - Jan 2023
A library for scientific machine learning and physics-informed learning
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:68 releases, 1835 reviews, 843 commits in 3 years 11 months
Contributions summary:Lu appears to have been primarily involved in refactoring and extending the SciCoNet/DeepXDE library. Their contributions include splitting the FNN module into separate FNN and model files, as well as adding support for various new functionalities such as the MIONet, different activation functions, variable saving/restoring, and different sampling methods. They also implemented examples utilizing the new features and provided support for both TensorFlow and PyTorch backends.
deeponetoperatorpaddlescientific-machine-learningtensorflow
lululxvi/oh-my-linux-apps
Jan 2019 - Nov 2022
Contributions:62 commits, 65 pushes, 1 branch in 3 years 10 months
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