Yuchao Lin
Research Assistant at Texas A&M University
College Station, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolYuchao Lin is a Ph.D. candidate and research assistant at Texas A&M University with eight years of research and engineering experience focused on invariant and equivariant geometric graph learning for 3D atomic systems. He has previously contributed to academic projects at Nanjing University and industry research internships at Fujitsu and Lambda, blending theoretical ML research with practical model integration. On GitHub he actively develops the divelab/DIG library, adapting and improving graph neural network modules like comenet for datasets such as MD17, QM9, and OC20. His work bridges deep learning architecture design and dataset-specific engineering, enabling more robust molecular and materials modeling. Based in College Station, Texas, he combines rigorous academic training with hands-on open-source contributions that accelerate research reproducibility.
8 years of coding experience
2 years of employment as a software developer
Bachelor's degree Computer Software Engineering, Bachelor's degree Computer Software Engineering at Nanjing UniversityDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Texas A&M University
Github Skills (7)
graph10
pytorch10
deep-learning10
3d-graphics10
graph-neural-network10
3d10
python9
Programming languages (6)
Github contributions (5)
divelab/DIG
Jun 2022 - Jun 2022
A library for graph deep learning research
Role in this project:
ML Engineer
ML EngineerContributions:13 commits, 4 PRs, 2 pushes in 2 days
Contributions summary:Yuchao's commits primarily focus on updating and modifying `comenet` codes within the `dig` repository, a library for graph deep learning research. These updates involved integrating the model with datasets such as MD17, QM9, and OC20, suggesting model adaptation and improvement for specific tasks. The changes include modifications to the `comenet`, `features`, and `radial_basis` modules, indicating an active involvement in the development and refinement of graph neural network architectures. The commits demonstrate a focus on improving the model's functionality and compatibility with various datasets within the research domain.
explainable-mlpytorchdataminingdeep-learninggraph-deep-learning
KruskalLin/Segmentation
May 2019 - Dec 2019
Models of segmentation applied in KITTI road datasets
Contributions:9 commits, 2 PRs, 4 pushes in 7 months
pytorchsegmentationkittisemantic-segmentationroad
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