Shaochen Zhong

695 total citations · 2 hit papers
6 papers, 278 citations indexed

About

Shaochen Zhong is a scholar working on Artificial Intelligence, Computer Networks and Communications and Molecular Biology. According to data from OpenAlex, Shaochen Zhong has authored 6 papers receiving a total of 278 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 1 paper in Computer Networks and Communications and 1 paper in Molecular Biology. Recurrent topics in Shaochen Zhong's work include Topic Modeling (2 papers), Advanced Data Storage Technologies (1 paper) and Computability, Logic, AI Algorithms (1 paper). Shaochen Zhong is often cited by papers focused on Topic Modeling (2 papers), Advanced Data Storage Technologies (1 paper) and Computability, Logic, AI Algorithms (1 paper). Shaochen Zhong collaborates with scholars based in United States. Shaochen Zhong's co-authors include Xia Hu, Hongye Jin, Jingfeng Yang, Bing Yin, Haoming Jiang, Qizhang Feng, Xiaotian Han, Ruixiang Tang, Daochen Zha and Fan Yang and has published in prestigious journals such as ACM Computing Surveys and ACM Transactions on Knowledge Discovery from Data.

In The Last Decade

Shaochen Zhong

4 papers receiving 263 citations

Hit Papers

align trajectories

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.

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

2024 237 citations Jingfeng Yang, Hongye Jin et al. ACM Transactions on Knowledge Discovery from Data profile →
Data-centric Artificial Intelligence: A Survey

2025 35 citations Daochen Zha, Kwei-Herng Lai et al. ACM Computing Surveys profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shaochen Zhong United States	3	140	42	28	27	23	6	278
Hongye Jin United States	2	128 0.9×	41 1.0×	24 0.9×	28 1.0×	20 0.9×	2	241
Giovanni Ciatto Italy	9	180 1.3×	60 1.4×	44 1.6×	14 0.5×	15 0.7×	35	289
Yun-Cheng Wang United States	8	196 1.4×	61 1.5×	24 0.9×	10 0.4×	62 2.7×	23	347
Roberta Calegari Italy	8	199 1.4×	39 0.9×	54 1.9×	16 0.6×	19 0.8×	44	294
G. Deepti Raj India	4	121 0.9×	21 0.5×	13 0.5×	41 1.5×	23 1.0×	10	269
Qingxiu Dong China	7	199 1.4×	39 0.9×	18 0.6×	10 0.4×	50 2.2×	11	297
Amr Mohamed El Koshiry Saudi Arabia	6	98 0.7×	53 1.3×	13 0.5×	8 0.3×	42 1.8×	13	261
Entesar Hamed I. Eliwa Saudi Arabia	7	98 0.7×	53 1.3×	13 0.5×	8 0.3×	44 1.9×	17	270
Shady Shehata Canada	10	245 1.8×	139 3.3×	37 1.3×	18 0.7×	42 1.8×	23	383
Azhana Ahmad Malaysia	9	99 0.7×	29 0.7×	24 0.9×	7 0.3×	41 1.8×	43	216

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-authorship network of co-authors of Shaochen Zhong

This figure shows the co-authorship network connecting the top 25 collaborators of Shaochen Zhong. A scholar is included among the top collaborators of Shaochen Zhong 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 Shaochen Zhong. Shaochen Zhong 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:

6 of 6 papers shown

1.

Zhong, Shaochen, Xinghuai Sun, Min Tian, et al.. (2025). LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem. 23009–23047.

2.

Zha, Daochen, Kwei-Herng Lai, Fan Yang, et al.. (2025). Data-centric Artificial Intelligence: A Survey. ACM Computing Surveys. 57(5). 1–42. 35 indexed citations breakdown →

3.

Zhong, Shaochen, Zirui Liu, Kaixiong Zhou, et al.. (2025). Flexible Group Count Enables Hassle-Free Structured Pruning. 4807–4818.

4.

Tan, Qiaoyu, et al.. (2024). Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering. 2056–2066. 2 indexed citations

5.

Yang, Jingfeng, Hongye Jin, Ruixiang Tang, et al.. (2024). Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond. ACM Transactions on Knowledge Discovery from Data. 18(6). 1–32. 237 indexed citations breakdown →

6.

Liu, Hongyi, et al.. (2024). KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches. 4623–4648. 4 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.

Explore authors with similar magnitude of impact