Shaochen Zhong

695 citations
6 papers · 278 · 2 hit papers · h-index 3

Impact in

    • Artificial Intelligence in Healthcare and Education
    • Topic Modeling
    • Natural Language Processing Techniques
    • Semantic Web and Ontologies
    • Privacy-Preserving Technologies in Data

Papers in

Shaochen Zhong

4 papers receiving 263 citations

Shaochen Zhong's Hit Papers

Data-centric Artificial Intelligence: A Survey 2025 · 35 citations
350+1Years since publication50100150200

Peers

Shaochen Zhong
Comparison fields: 5 of 81
  • Health Informatics 27
  • Artificial Intelligence 140
  • Software 9
  • Information Systems 42
  • Management Information Systems 15
Replace Hongye Jin with:
Hongye Jin United States
Yun-Cheng Wang United States
Yanghe Pan China
Damai Dai China
Doris Xin United States
Hengyi Cai China
Jian Kang United States
Kshitiz Aryal United States
Vivek Kumar Italy
Jörg Schlötterer Germany
Shaochen Zhong relative to Hongye Jin United States Hongye Jin's profile →
Citations per field
00.5×1.5×
Hongye Jin · 1×
Citations per year

Countries citing papers authored by Shaochen Zhong

Since Specialization
Citations

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

Fields of papers citing papers by Shaochen Zhong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Shaochen Zhong, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Shaochen Zhong Line = papers co-authored together Shaochen Zhong links everyone, so they are left out of the graph.

All Works

6 of 6 papers shown
#Work
1
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
Hit paper breakdown →
2024237
2
Data-centric Artificial Intelligence: A Survey
Hit paper breakdown →
202535
3 20244
4 20242
5 20250
6 20250

About Shaochen Zhong

Shaochen Zhong is a scholar working on Artificial Intelligence, Computer Networks and Communications, Molecular Biology, Information Systems and Health Informatics, having authored 6 papers that have together received 278 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Privacy-Preserving Technologies in Data (1 paper), Data Stream Mining Techniques (1 paper), Expert finding and Q&A systems (1 paper), Natural Language Processing Techniques (1 paper), Machine Learning and Data Classification (1 paper), Parallel Computing and Optimization Techniques (1 paper) and Advanced Data Storage Technologies (1 paper). The work is most often cited by research in Health Informatics (27 citations), Artificial Intelligence (140 citations), Software (9 citations), Information Systems (42 citations) and Management Information Systems (15 citations). Shaochen Zhong has collaborated with scholars based in United States. Frequent co-authors include Xia Hu, Xiaotian Han, Qizhang Feng, Jingfeng Yang, Bing Yin, Ruixiang Tang, Haoming Jiang, Hongye Jin, Daochen Zha and Kwei-Herng Lai. Their work appears in journals such as ACM Computing Surveys and ACM Transactions on Knowledge Discovery from Data.

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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