Shi Han

2.9k total citations
84 papers, 1.7k citations indexed

About

Shi Han is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Shi Han has authored 84 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 31 papers in Information Systems and 20 papers in Computer Vision and Pattern Recognition. Recurrent topics in Shi Han's work include Topic Modeling (21 papers), Natural Language Processing Techniques (14 papers) and Software Engineering Research (13 papers). Shi Han is often cited by papers focused on Topic Modeling (21 papers), Natural Language Processing Techniques (14 papers) and Software Engineering Research (13 papers). Shi Han collaborates with scholars based in China, United States and Hong Kong. Shi Han's co-authors include Dongmei Zhang, Tao Xie, Lun Du, Yingnong Dang, Bin Yu, Ge Song, Yanlin Wang, Rui Ding, Junqi Chen and Simin Liu and has published in prestigious journals such as Expert Systems with Applications, IEEE Transactions on Wireless Communications and IEEE Transactions on Software Engineering.

In The Last Decade

Shi Han

75 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shi Han China	24	591	571	389	304	282	84	1.7k
Qinbao Song China	20	320 0.5×	1.1k 1.9×	196 0.5×	67 0.2×	403 1.4×	53	1.7k
Michèle Sébag France	24	333 0.6×	1.0k 1.8×	323 0.8×	68 0.2×	163 0.6×	86	1.7k
David Parker United Kingdom	31	232 0.4×	793 1.4×	503 1.3×	753 2.5×	133 0.5×	125	2.6k
H.H. Ammar United States	22	884 1.5×	659 1.2×	447 1.1×	798 2.6×	194 0.7×	134	1.9k
Saeed Jalili Iran	18	335 0.6×	510 0.9×	252 0.6×	64 0.2×	135 0.5×	92	981
Jitender Kumar Chhabra India	21	782 1.3×	699 1.2×	298 0.8×	503 1.7×	185 0.7×	95	1.5k
Yves Deville Belgium	18	116 0.2×	419 0.7×	566 1.5×	144 0.5×	293 1.0×	81	1.7k
Shaowei Cai China	24	230 0.4×	674 1.2×	798 2.1×	219 0.7×	84 0.3×	113	1.9k
Milind Kulkarni United States	23	426 0.7×	448 0.8×	1.5k 3.9×	120 0.4×	433 1.5×	116	2.3k
Nicola Mazzocca Italy	21	569 1.0×	420 0.7×	753 1.9×	168 0.6×	105 0.4×	181	1.7k

Countries citing papers authored by Shi Han

Since Specialization

Citations

This map shows the geographic impact of Shi Han'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 Shi Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shi Han more than expected).

Fields of papers citing papers by Shi Han

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shi Han. 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 Shi Han. The network helps show where Shi Han may publish in the future.

Co-authorship network of co-authors of Shi Han

This figure shows the co-authorship network connecting the top 25 collaborators of Shi Han. A scholar is included among the top collaborators of Shi Han 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 Shi Han. Shi Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wang, Yanlin, Ensheng Shi, Lun Du, et al.. (2025). Context-aware code summarization with multi-relational graph neural network. Automated Software Engineering. 32(1).

Guo, Jiwei, et al.. (2024). Experiments on the performance of low temperature air source heat pump based on parallel connection of evaporators. Journal of Building Engineering. 98. 111021–111021. 1 indexed citations

Han, Shi, et al.. (2024). FXAM: A unified and fast interpretable model for predictive analytics. Expert Systems with Applications. 252. 123890–123890. 2 indexed citations

Zhou, Mengyu, Lun Du, Yan Gao, et al.. (2024). Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries. Proceedings of the AAAI Conference on Artificial Intelligence. 38(16). 18206–18215. 6 indexed citations

Sui, Yuan, Mengyu Zhou, Mingjie Zhou, Shi Han, & Dongmei Zhang. (2024). Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study. 645–654. 43 indexed citations

Chen, Xu, Qiang Fu, Lun Du, et al.. (2024). Text-to-Image Generation for Abstract Concepts. Proceedings of the AAAI Conference on Artificial Intelligence. 38(4). 3360–3368. 3 indexed citations

Zhou, Mengyu, et al.. (2024). Vision Language Models for Spreadsheet Understanding: Challenges and Opportunities. 116–128.

Sui, Yuan, et al.. (2024). TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning. 10306–10323. 3 indexed citations

Li, Qiyu, et al.. (2023). SheetPT: Spreadsheet Pre-training Based on Hierarchical Attention Network. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 12951–12958.

10.

Fu, Qiang, Lun Du, Jian–Guang Lou, et al.. (2023). Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models. 276–285. 5 indexed citations

11.

Fan, Zhichao, et al.. (2023). HermEs: Interactive Spreadsheet Formula Prediction via Hierarchical Formulet Expansion. 8356–8372. 4 indexed citations

12.

Han, Shi, et al.. (2022). FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1150–1166. 9 indexed citations

13.

Wang, Zhiruo, Ran Jia, Jiaqi Guo, et al.. (2022). HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1094–1110. 20 indexed citations

14.

Shi, Ensheng, Yanlin Wang, Lun Du, et al.. (2021). CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 4053–4062. 26 indexed citations

15.

Zhou, Mengyu, et al.. (2020). Table2Charts: Learning Shared Representations for Recommending Charts on Multi-dimensional Data. arXiv (Cornell University). 3 indexed citations

16.

Han, Shi, et al.. (2019). Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS. arXiv (Cornell University). 8 indexed citations

17.

Han, Shi, et al.. (2015). Design study of dedicated brain PET with polyhedron geometry. Technology and Health Care. 23(2_suppl). S615–S623. 6 indexed citations

18.

Xiao, Xusheng, Shi Han, Dongmei Zhang, & Tao Xie. (2013). Context-sensitive delta inference for identifying workload-dependent performance bottlenecks. 90–100. 54 indexed citations

19.

Han, Shi, Yingnong Dang, Ge Song, Dongmei Zhang, & Tao Xie. (2012). Performance debugging in the large via mining millions of stack traces. International Conference on Software Engineering. 145–155. 37 indexed citations

20.

Han, Shi, Yingnong Dang, Ge Song, Dongmei Zhang, & Tao Xie. (2012). Performance debugging in the large via mining millions of stack traces. 145–155. 122 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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