Daya Guo
Research Intern at 微软
Haidian District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolDaya Guo is a research-focused NLP engineer and PhD candidate in a SYSU–MSRA joint program with eight years of hands-on experience building and optimizing machine learning pipelines. Currently a research intern at Microsoft in Beijing, Daya has strong expertise in NLP, ML/DL algorithms, and practical data mining demonstrated by top-ranked code for the 2020 Tencent College Algorithm Contest. He contributes to prominent open-source efforts such as CodeXGLUE on clone and defect detection, showing applied skills in code analysis and model evaluation. A high-achieving computer scientist (ranked 1/88 in his undergraduate cohort), he combines rigorous academic training with competitive data-science experience on platforms like Kaggle and Tianchi. Notably, his work spans both model architecture adjustments and end-to-end training pipeline improvements, reflecting a knack for squeezing performance gains across the stack.
7 years of coding experience
博士, 计算机科学与技术, 博士, 计算机科学与技术 at 中山大学Bachelor's degree, Computer Science, GPA: 4.3/5 Rank: 1/88, Bachelor's degree, Computer Science, GPA: 4.3/5 Rank: 1/88 at Sun Yat-Sen University
Github Skills (15)
pytorch10
machine-learning10
code-analysis10
nlp10
tensorflow10
python10
word2vec10
natural-language-processing10
data-analysis9
pandas9
algorithms8
algorithm8
modeling8
trainings8
computer-vision7
Programming languages (5)
Github contributions (5)
guoday/Tencent2020_Rank1st
Jul 2020 - Jun 2022
The code for 2020 Tencent College Algorithm Contest, and the online result ranks 1st.
Role in this project:
Data Scientist
Data ScientistContributions:18 commits, 1 PR, 8 pushes in 1 year 10 months
Contributions summary:Daya primarily focused on updating and refining machine learning model components. They modified preprocessing steps in `preprocess.py`, and adjusted model architectures in `model.py` to incorporate features and model layers, likely for improving performance. Further, the user updated training pipeline in `run.py` for training configuration, and made adjustments to the word embedding generation and usage in `w2v.py` with the `Word2Vec` model. These changes suggest an active role in optimizing the overall machine learning pipeline.
javascriptrankstencent
microsoft/CodeXGLUE
Sep 2020 - Nov 2021
CodeXGLUE
Role in this project:ML Engineer
ML EngineerContributions:8 commits, 2 PRs, 49 comments in 1 year 2 months
Contributions summary:Daya primarily worked on the `Clone-detection-POJ-104`, `Clone-detection-BigCloneBench`, and `Defect-detection` tasks. Their work involved updating and modifying the run scripts, model training, and evaluation. The changes suggest they were involved in tasks relating to clone detection and defect detection, which indicates that they were working on machine learning models for code analysis.
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