Daya Guo

6.5k total citations · 4 hit papers
21 papers, 2.4k citations indexed

About

Daya Guo is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Daya Guo has authored 21 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 6 papers in Information Systems and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Daya Guo's work include Topic Modeling (13 papers), Natural Language Processing Techniques (10 papers) and Multimodal Machine Learning Applications (5 papers). Daya Guo is often cited by papers focused on Topic Modeling (13 papers), Natural Language Processing Techniques (10 papers) and Multimodal Machine Learning Applications (5 papers). Daya Guo collaborates with scholars based in China, United Kingdom and United States. Daya Guo's co-authors include Nan Duan, Ming Zhou, Duyu Tang, Daxin Jiang, Zhangyin Feng, Linjun Shou, Ming Gong, Xiaocheng Feng, Ting Liu and Bing Qin and has published in prestigious journals such as IEEE Transactions on Software Engineering, Automated Software Engineering and Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

In The Last Decade

Daya Guo

19 papers receiving 2.3k citations

Hit Papers

CodeBERT: A Pre-Trained Model for Programming and Natural... 2020 2026 2022 2024 2020 2022 2021 2022 400 800 1.2k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. CodeBERT: A Pre-Trained Model for Programming and Natural Languages
    2020 1.4k citations Zhangyin Feng, Daya Guo et al. profile →
  2. UniXcoder: Unified Cross-Modal Pre-training for Code Representation
    2022 302 citations Daya Guo, Shuai Lu et al. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) profile →
  3. GraphCodeBERT: Pre-training Code Representations with Data Flow
    2021 261 citations Daya Guo, Shuo Ren et al. profile →
  4. Automating code review activities by large-scale pre-training
    2022 92 citations Zhiyu Li, Shuai Lu et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daya Guo China 11 1.6k 1.3k 766 483 380 21 2.4k
Ge Li China 25 1.6k 1.0× 1.0k 0.8× 819 1.1× 532 1.1× 406 1.1× 108 2.2k
Linjun Shou China 14 1.1k 0.7× 1.2k 0.9× 521 0.7× 351 0.7× 269 0.7× 39 2.0k
Işıl Dillig United States 27 1.3k 0.8× 1.2k 1.0× 1.2k 1.5× 717 1.5× 700 1.8× 98 2.4k
Benoît Baudry France 27 1.4k 0.9× 1.0k 0.8× 1.5k 2.0× 237 0.5× 544 1.4× 126 2.3k
Hridesh Rajan United States 21 1.3k 0.9× 998 0.8× 512 0.7× 197 0.4× 628 1.7× 140 1.9k
Abdelwahab Hamou‐Lhadj Canada 23 1.1k 0.7× 623 0.5× 659 0.9× 410 0.8× 926 2.4× 140 1.7k
Spiros Mancoridis United States 23 2.1k 1.3× 1.3k 1.0× 976 1.3× 332 0.7× 985 2.6× 83 2.5k
Leon Moonen Norway 26 2.4k 1.6× 1.0k 0.8× 1.5k 2.0× 288 0.6× 874 2.3× 100 2.8k
Anna Rita Fasolino Italy 27 1.8k 1.2× 541 0.4× 1.5k 2.0× 836 1.7× 780 2.1× 105 2.6k
Ralf Lämmel Germany 24 1.3k 0.8× 1.6k 1.2× 733 1.0× 126 0.3× 557 1.5× 115 2.2k

Countries citing papers authored by Daya Guo

Since Specialization
Citations

This map shows the geographic impact of Daya Guo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daya Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daya Guo more than expected).

Fields of papers citing papers by Daya Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daya Guo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daya Guo. The network helps show where Daya Guo may publish in the future.

Co-authorship network of co-authors of Daya Guo

This figure shows the co-authorship network connecting the top 25 collaborators of Daya Guo. A scholar is included among the top collaborators of Daya Guo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daya Guo. Daya Guo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Guo, Daya, Jiachi Chen, John Grundy, et al.. (2025). RepoTransBench: A Real-World Multilingual Benchmark for Repository-Level Code Translation. IEEE Transactions on Software Engineering. 52(2). 675–690. 2 indexed citations
2.
Guo, Daya, et al.. (2025). An Empirical Study of Exploring the Capabilities of Large Language Models in Code Learning. IEEE Transactions on Software Engineering. 51(11). 3088–3102.
3.
Wang, Yanlin, Ensheng Shi, Lun Du, et al.. (2025). Context-aware code summarization with multi-relational graph neural network. Automated Software Engineering. 32(1).
4.
Wang, Yanlin, et al.. (2024). SparseCoder: Identifier-Aware Sparse Transformer for File- Level Code Summarization. 614–625. 8 indexed citations
5.
Xu, Canwen, Daya Guo, Nan Duan, & Julian McAuley. (2023). Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data. 6268–6278. 59 indexed citations
6.
Zhang, Hang, Yeyun Gong, Xingwei He, et al.. (2023). Noisy Pair Corrector for Dense Retrieval. 11439–11451. 1 indexed citations
7.
Li, Xiaonan, Daya Guo, Yeyun Gong, et al.. (2022). Soft-Labeled Contrastive Pre-Training for Function-Level Code Representation. 118–129. 5 indexed citations
8.
Xu, Canwen, Daya Guo, Nan Duan, & Julian McAuley. (2022). LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval. Findings of the Association for Computational Linguistics: ACL 2022. 3557–3569. 14 indexed citations
9.
Li, Zhiyu, Shuai Lu, Daya Guo, et al.. (2022). Automating code review activities by large-scale pre-training. 1035–1047. 92 indexed citations breakdown →
10.
Zhong, Wanjun, Siyuan Wang, Duyu Tang, et al.. (2022). Analytical Reasoning of Text. 2306–2319. 4 indexed citations
11.
Guo, Daya, Shuai Lu, Nan Duan, et al.. (2022). UniXcoder: Unified Cross-Modal Pre-training for Code Representation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 7212–7225. 302 indexed citations breakdown →
12.
Guo, Daya, Duyu Tang, Qinliang Su, et al.. (2021). Syntax-Enhanced Pre-trained Model. 5412–5422. 20 indexed citations
13.
Guo, Daya, Shuo Ren, Shuai Lu, et al.. (2021). GraphCodeBERT: Pre-training Code Representations with Data Flow. 261 indexed citations breakdown →
14.
Guo, Daya, Duyu Tang, Nan Duan, et al.. (2020). Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder. 6118–6129. 7 indexed citations
15.
Feng, Zhangyin, Daya Guo, Duyu Tang, et al.. (2020). CodeBERT: A Pre-Trained Model for Programming and Natural Languages. 1536–1547. 1356 indexed citations breakdown →
16.
Lv, Shangwen, Daya Guo, Jingjing Xu, et al.. (2020). Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering. Proceedings of the AAAI Conference on Artificial Intelligence. 34(5). 8449–8456. 115 indexed citations
17.
Shen, Tao, Xiubo Geng, Tao Qin, et al.. (2019). Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base. 2442–2451. 54 indexed citations
18.
Guo, Daya, et al.. (2019). Multi-modal Representation Learning for Short Video Understanding and Recommendation. 687–690. 5 indexed citations
19.
Guo, Daya, Duyu Tang, Nan Duan, Ming Zhou, & Jian Yin. (2018). Dialog-to-action: conversational question answering over a large-scale knowledge base. Neural Information Processing Systems. 31. 2946–2955. 43 indexed citations
20.
Guo, Daya, Yibo Sun, Duyu Tang, et al.. (2018). Question Generation from SQL Queries Improves Neural Semantic Parsing. 1597–1607. 34 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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