Yawei Li
Doctoral Student at Ludwig-Maximilians-Universität München
Munich, Bavaria, Germany
Summary
👤
Senior🎓
Top SchoolYawei Li is a PhD candidate at Ludwig-Maximilians-Universität München with eight years of experience bridging electrical engineering, biomedical computing, and machine learning. He has held research and industry roles—including a visiting researcher stint at NUS, an internship at SAP, and a recent Applied Scientist position at Amazon—that reflect a track record of translating research into applied systems. Yawei contributes to prominent open-source ML tooling, notably improving the EMA handler in pytorch/ignite to better support data-parallel and distributed training, showing attention to robustness and API usability. His background spans clinical laboratory training to advanced computational methods, giving him a rare blend of domain knowledge and practical ML engineering. Based in Munich and actively seeking opportunities, he brings rigorous academic training plus hands-on experience shipping reliable ML components in production-like settings.
8 years of coding experience
2 years of employment as a software developer
Technischen Universität DarmstadtDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Ludwig-Maximilians-Universität MünchenMaster's degree Biomedical Computing, Master's degree Biomedical Computing at Technical University of Munich学士 临床检验, 学士 临床检验 at Southern Medical University
Github Skills (8)
neural-network10
pytorch10
machine-learning10
deeplearning-ai10
deep-learning10
python10
metric9
testing8
Programming languages (8)
Github contributions (5)
pytorch/ignite
Jul 2021 - Jun 2022
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
Role in this project:
ML Engineer
ML EngineerContributions:28 reviews, 6 commits, 6 PRs in 11 months
Contributions summary:Yawei's contributions primarily revolve around enhancing the Exponential Moving Average (EMA) handler within the `pytorch/ignite` repository, which focuses on deep learning and PyTorch. The user implemented and refined the EMA handler, including adding features, addressing potential issues with data parallelism and distributed training, and introducing flexibility in its behavior. The user also improved the API and documentation for the EMA handler.
pytorchpythondeep-learninghigh-levelneural-networks
sandylaker/ignite
Jul 2021 - Jun 2022
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
Contributions:80 pushes, 11 branches in 11 months
pytorchdeep-learninghigh-levelneural-networksmachine-learning
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