Jimmy Lin

22.3k total citations · 1 hit paper
431 papers, 11.1k citations indexed

About

Jimmy Lin is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jimmy Lin has authored 431 papers receiving a total of 11.1k indexed citations (citations by other indexed papers that have themselves been cited), including 314 papers in Artificial Intelligence, 161 papers in Information Systems and 95 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jimmy Lin's work include Topic Modeling (212 papers), Natural Language Processing Techniques (139 papers) and Information Retrieval and Search Behavior (53 papers). Jimmy Lin is often cited by papers focused on Topic Modeling (212 papers), Natural Language Processing Techniques (139 papers) and Information Retrieval and Search Behavior (53 papers). Jimmy Lin collaborates with scholars based in United States, Canada and United Kingdom. Jimmy Lin's co-authors include Dina Demner‐Fushman, Chris Dyer, Boris Katz, Rodrigo Nogueira, Hua He, Raphael Tang, Michael C. Schatz, Aneesh Sharma, W. John Wilbur and Pankaj Gupta and has published in prestigious journals such as Genome biology, BMC Bioinformatics and Hydrological Processes.

In The Last Decade

Jimmy Lin

405 papers receiving 10.2k citations

Hit Papers

Data-intensive text processing with MapReduce 2009 2026 2014 2020 2009 50 100 150 200 250
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. Data-intensive text processing with MapReduce
    2009 280 citations Jimmy Lin 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
Jimmy Lin United States 58 7.4k 4.1k 2.1k 1.7k 1.0k 431 11.1k
Steffen Staab Germany 47 7.4k 1.0× 4.6k 1.1× 851 0.4× 2.0k 1.1× 1.3k 1.3× 335 10.3k
Berthier Ribeiro‐Neto Brazil 25 5.1k 0.7× 5.0k 1.2× 1.9k 0.9× 2.0k 1.1× 527 0.5× 68 9.3k
ChengXiang Zhai United States 66 11.4k 1.6× 8.0k 2.0× 2.4k 1.1× 1.1k 0.6× 1.5k 1.4× 386 16.9k
Oren Etzioni United States 63 13.5k 1.8× 6.4k 1.6× 1.5k 0.7× 2.1k 1.2× 829 0.8× 192 17.7k
Stephen Robertson United Kingdom 45 8.3k 1.1× 7.2k 1.8× 2.2k 1.1× 1.0k 0.6× 608 0.6× 180 12.3k
Gerhard Weikum Germany 57 7.9k 1.1× 4.0k 1.0× 1.4k 0.6× 4.9k 2.8× 661 0.7× 472 12.8k
Xueqi Cheng China 52 7.2k 1.0× 3.4k 0.8× 1.5k 0.7× 1.3k 0.7× 426 0.4× 517 11.6k
Maarten de Rijke Netherlands 50 9.7k 1.3× 5.7k 1.4× 1.4k 0.7× 990 0.6× 286 0.3× 659 13.1k
Irwin King Hong Kong 56 7.9k 1.1× 8.0k 2.0× 3.7k 1.8× 2.8k 1.6× 457 0.5× 404 15.4k
George W. Furnas United States 32 7.3k 1.0× 4.4k 1.1× 4.1k 2.0× 1.0k 0.6× 597 0.6× 59 14.0k

Countries citing papers authored by Jimmy Lin

Since Specialization
Citations

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

Fields of papers citing papers by Jimmy Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jimmy Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Jimmy Lin. A scholar is included among the top collaborators of Jimmy Lin 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 Jimmy Lin. Jimmy Lin 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.
Ma, Xueguang, Shengyao Zhuang, Bevan Koopman, et al.. (2025). VISA: Retrieval Augmented Generation with Visual Source Attribution. 30154–30169.
2.
Ogueji, Kelechi, et al.. (2025). AfroBench: How Good are Large Language Models on African Languages?. 19048–19095.
3.
Pradeep, Ronak, Nandan Thakur, Daniel Campos, et al.. (2025). The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models. 180–190.
4.
5.
Pradeep, Ronak, Daniel Lee, Ali Mousavi, et al.. (2024). ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA Datasets with Large Language Models. 1176–1206. 3 indexed citations
6.
Zhang, Yingxue, Xiaoguang Li, Jianye Hao, et al.. (2024). EWEK-QA : Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems. 14169–14187. 1 indexed citations
7.
Ma, Xueguang, Sheng-Chieh Lin, Minghan Li, Wenhu Chen, & Jimmy Lin. (2024). Unifying Multimodal Retrieval via Document Screenshot Embedding. 6492–6505. 4 indexed citations
8.
Thakur, Nandan, Jianmo Ni, Gustavo Hernández Ábrego, et al.. (2024). Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval. 7699–7724.
9.
Zhuang, Honglei, Kai Hui, Zhen Qin, et al.. (2024). Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?. 2321–2326.
10.
Kamalloo, Ehsan, et al.. (2024). Towards Robust QA Evaluation via Open LLMs. 2811–2816. 4 indexed citations
11.
Pradeep, Ronak, et al.. (2023). How Does Generative Retrieval Scale to Millions of Passages?. 1305–1321. 10 indexed citations
12.
Piktus, Aleksandra, et al.. (2023). GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration. IRIS Research product catalog (Sapienza University of Rome). 588–598.
13.
Kamalloo, Ehsan, et al.. (2023). Evaluating Embedding APIs for Information Retrieval. 518–526. 7 indexed citations
14.
Zhong, Wei, Sheng-Chieh Lin, Jheng-Hong Yang, & Jimmy Lin. (2023). One Blade for One Purpose: Advancing Math Information Retrieval using Hybrid Search. 141–151. 2 indexed citations
15.
Duh, Kevin, et al.. (2022). AfriCLIRMatrix: Enabling Cross-Lingual Information Retrieval for African Languages. 8721–8728. 7 indexed citations
16.
Yang, Jheng-Hong, et al.. (2022). Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval. 1092–1102. 9 indexed citations
17.
Gauch, Martin, Juliane Mai, & Jimmy Lin. (2020). The proper care and feeding of CAMELS: How limited training data affects streamflow prediction. Environmental Modelling & Software. 135. 104926–104926. 109 indexed citations
18.
Pradeep, Ronak, et al.. (2020). H2oloo at TREC 2020: When all you got is a hammer... Deep Learning, Health Misinformation, and Precision Medicine.. Text REtrieval Conference. 4 indexed citations
19.
Zou, Yanyan, et al.. (2019). Aligning Cross-Lingual Entities with Multi-Aspect Information. 4430–4440. 98 indexed citations
20.
Lin, Jimmy, Donald Metzler, Tamer Elsayed, & Lidan Wang. (2009). Of Ivory and Smurfs: Loxodontan MapReduce Experiments for Web Search. Text REtrieval Conference. 33 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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