Daya Guo
Also known as 郭达雅, Guo Daya
Daya Guo is an AI researcher whose work on code intelligence and reasoning appears in several DeepSeek releases. He studied computer science at Sun Yat-sen University and pursued a PhD there until 2023. In 2020 he received a Microsoft Research Asia Fellowship and co-authored CodeBERT, a pre-trained model for programming and natural languages. His contributions include DeepSeek-Coder, DeepSeek-V3 and DeepSeek-R1, which were posted to arXiv between January 2024 and January 2025. From July 2023 to April 2026 he worked as an AI researcher at DeepSeek.
- Now at
- Sun Yat-sen University
- Works
- 73
- Citations
- 7,116
- h-index
- 23
Publication numbers from OpenAlex, as of 15 October 2026.
Timeline
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2014–2018
Studying at Sun Yat-sen University
He studies computer science at Sun Yat-sen University from August 2014 to June 2018.
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2018–2023
A PhD in computer science
He studies for a PhD in computer science at Sun Yat-sen University from August 2018 to June 2023.
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2020
A Microsoft Research Asia fellowship
In 2020 he receives the Microsoft Research Asia Fellowship, given to 12 outstanding PhD students in the Asia-Pacific region, and he co-authors CodeBERT, a pre-trained model for programming and natural languages.
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July 2023–April 2026
An AI researcher at DeepSeek
He works as an AI researcher at DeepSeek from July 2023 to April 2026, focusing on code intelligence and LLM reasoning.
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January 2024
Contributing to DeepSeek-Coder
He is a core contributor to DeepSeek-Coder, which is posted to arXiv in January 2024.
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December 2024–January 2025
Core work on DeepSeek-V3 and R1
He is a core contributor to DeepSeek-V3 and DeepSeek-R1, which are posted to arXiv in December 2024 and January 2025.
Sources and credits
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- Daya Guo
- CodeBERT: A Pre-Trained Model for Programming and Natural Languages - ACL Anthology
- [2401.14196] DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
- [2412.19437] DeepSeek-V3 Technical Report
- [2501.12948] DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning