Feng Ziyuan

Shanghai, Shanghai, China

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Summary

🤩

Rockstar

🎓

Top School

Feng Ziyuan is a machine learning engineer with nine years of experience blending data insight, model understanding, and strong coding ability to deliver practical ML solutions. Trained in data-driven modeling at HKUST and computer science at Fudan, he excels at building and refining neural models—evidenced by contributions to the fastNLP project where he implemented embeddings, attention, and LSTM components for character-level language modeling. Based in Shanghai, he pairs rigorous academic grounding with hands-on backend development, validation-driven training workflows, and a pragmatic “better code than never” ethos. Colleagues rely on him to move models from experimentation toward robust, well-tested implementations while keeping an eye on reproducibility and model selection.

9 years of coding experience

Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Fudan University

Summer School International Program, Approximation Algorithms, Scheduling, Game Theory, A, Summer School International Program, Approximation Algorithms, Scheduling, Game Theory, A at Peking University

Hong Kong University of Science and Technology (HKUST)

Chinese, English, Chinese

Github Skills (17)

tokenize10

pytorch10

python10

machine-learning10

ml10

cross-entropy10

tokenizer10

lstm10

deep-learning10

natural-language-processing10

neural-network10

nlp10

parameter-tuning9

trainings9

optimisation9

Programming languages (3)

JavaC++Python

Github contributions (5)

fastnlp/fastNLP

Mar 2018 - Mar 2019

fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.

Role in this project:

Back-end Developer & ML Engineer

Contributions:4 releases, 261 commits, 73 PRs in 1 year

Contributions summary:Feng primarily worked on developing and improving a character-level neural language model, as indicated by the code changes and commit messages. Their contributions included implementing core components like embeddings, attention mechanisms, and a long short-term memory (LSTM) network. The user also focused on training the model, incorporating validation steps, and saving the best-performing versions.

nlpbertword-embeddingsdeep-learningtext-processing

FengZiYjun/Matrix-Toolbox

Sep 2017 - Jun 2018

Contributions:138 commits, 69 pushes, 5 branches in 8 months

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