Zhanglong Ji

726 citations
12 papers · 343 indexed · h-index 9
Topics
Privacy-Preserving Technologies in Data (7 papers)Cryptography and Data Security (3 papers)Privacy, Security, and Data Protection (3 papers)

In The Last Decade

Zhanglong Ji

12 papers receiving 336 citations

Peers

Zhanglong Ji
Comparison fields: 5 of 62
  • Artificial Intelligence 246
  • Public Health, Environmental and Occupational Health 68
  • Statistics and Probability 53
  • Cancer Research 33
  • Genetics 33
Replace Jill Muehling with:
Jill Muehling United States
Luca Bonomi United States
João Sá Sousa Portugal
Jean Louis Raisaro Switzerland
Md Momin Al Aziz Canada
Braden Soper United States
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Yongkai Wu United States
Ziran Li China
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Zhanglong Ji relative to Jill Muehling United States Jill Muehling's profile →
Citations per field
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Citations per year

Countries citing papers authored by Zhanglong Ji

Since Specialization
Citations

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

Fields of papers citing papers by Zhanglong Ji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhanglong Ji

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

All Works

12 of 12 papers shown
#WorkIndexed citations
1 6
2 7
3 24
4 17
5 61
6 94
7 35
8 39
9 26
10
When you can't tell when it hurts: a preliminary algorithm to assess pain in patients who can't communicate.
3
11
Differential-Private Data Publishing Through Component Analysis.
15
12 16

About Zhanglong Ji

Zhanglong Ji is a scholar working on Computer Science Applications, Statistics and Probability and Artificial Intelligence, having authored 12 papers that have together received 343 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (7 papers), Cryptography and Data Security (3 papers) and Privacy, Security, and Data Protection (3 papers). The work is most often cited by research in Health Informatics (20 citations), Artificial Intelligence (246 citations) and Statistics and Probability (53 citations). Zhanglong Ji has collaborated with scholars based in United States, Switzerland and Hong Kong. Frequent co-authors include Xiaoqian Jiang, Shuang Wang, Lucila Ohno‐Machado, Li Xiong, Fei Yu, Chia-Lun Lu, Charles Elkan, Yuan Wu, Haoran Li and Weizhu Chen. Their work appears in journals such as Bioinformatics, IEEE Transactions on Knowledge and Data Engineering and Machine Learning.

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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