Yifan Li
Intern at CA State Water Resources Control Board
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYifan Li is an intern and 2nd-year PhD student with six years of hands-on experience bridging environmental engineering and machine learning, currently based in Los Angeles. He combines field and lab experience in hydrogeology and soil contamination with practical regulatory work at the California State Water Resources Control Board, giving him uncommon domain depth for an ML practitioner. On GitHub he contributes to RecBole, implementing the DCNV2 model in PyTorch to extend a prominent recommendation library, demonstrating strong back-end and ML engineering skills. His background includes groundwater sampling, contaminant fixation experiments, and CIWQS administrative workflows, which inform a data-driven approach to environmental problems. Yifan’s profile reflects a rare blend of rigorous environmental science, regulatory experience, and applied deep learning—positioning him to translate complex environmental data into deployable ML solutions.
6 years of coding experience
1 year of employment as a software developer
Master's degree, Environmental Enigneering, GPA: 3.36/4, Master's degree, Environmental Enigneering, GPA: 3.36/4 at University of Southern CaliforniaBachelor's degree, Hydrology and Water Resources Science, Bachelor's degree, Hydrology and Water Resources Science at China University of Geosciences
Github Skills (5)
pytorch10
deeplearning-ai10
recommendation-system10
deep-learning10
python10
Programming languages (2)
Github contributions (5)
RUCAIBox/RecBole
Sep 2022 - Dec 2022
A unified, comprehensive and efficient recommendation library
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:3 reviews, 47 commits, 23 PRs in 3 months
Contributions summary:Yifan contributed significantly to the `DCNV2` model, adding the core implementation in `dcnv2.py`. Their work involved integrating this advanced deep learning model into the `recbole` framework. This included defining the model architecture, loss functions, and forward pass, indicating a strong understanding of PyTorch and recommendation system principles. The user's focus appears to be on extending the library with cutting-edge recommendation models.
pytorchrecommenderdeep-learningcollaborative-filteringsequential-recommendation
AoiDragon/AoiDragon.github.io
Mar 2022 - May 2022
Contributions:6 commits, 2 pushes, 1 branch in 1 month
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