Qing Liu

4.0k total citations · 2 hit papers
128 papers, 2.5k citations indexed

About

Qing Liu is a scholar working on Computer Networks and Communications, Hardware and Architecture and Information Systems. According to data from OpenAlex, Qing Liu has authored 128 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Computer Networks and Communications, 35 papers in Hardware and Architecture and 29 papers in Information Systems. Recurrent topics in Qing Liu's work include Advanced Data Storage Technologies (45 papers), Parallel Computing and Optimization Techniques (33 papers) and Distributed and Parallel Computing Systems (18 papers). Qing Liu is often cited by papers focused on Advanced Data Storage Technologies (45 papers), Parallel Computing and Optimization Techniques (33 papers) and Distributed and Parallel Computing Systems (18 papers). Qing Liu collaborates with scholars based in China, United States and Hong Kong. Qing Liu's co-authors include MengChu Zhou, Haoyue Liu, Norbert Podhorszki, Haitao Yuan, Dongxiang Zhang, Loo Hay Lee, Wang Yuan, Fumin Shen, Scott Klasky and Matthew Wolf and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Geophysical Research Atmospheres and Langmuir.

In The Last Decade

Qing Liu

123 papers receiving 2.5k citations

Hit Papers

An embedded feature selection method for imbalanced data ... 2016 2026 2019 2022 2019 2016 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qing Liu China 26 861 683 414 306 269 128 2.5k
Paolo Nesi Italy 27 739 0.9× 610 0.9× 833 2.0× 373 1.2× 116 0.4× 240 3.3k
Aiiad Albeshri Saudi Arabia 26 921 1.1× 743 1.1× 583 1.4× 208 0.7× 63 0.2× 92 2.3k
Marcin Paprzycki Poland 17 705 0.8× 550 0.8× 384 0.9× 124 0.4× 139 0.5× 215 1.8k
Shanika Karunasekera Australia 26 1.5k 1.7× 1.4k 2.1× 952 2.3× 120 0.4× 71 0.3× 158 3.3k
David Taniar Australia 36 1.7k 2.0× 1.1k 1.6× 1.1k 2.7× 242 0.8× 110 0.4× 361 4.5k
Paul A. Fishwick United States 26 542 0.6× 769 1.1× 260 0.6× 108 0.4× 106 0.4× 201 3.0k
Huy T. Vo United States 24 828 1.0× 386 0.6× 529 1.3× 275 0.9× 485 1.8× 72 2.8k
Shengzhong Feng China 30 513 0.6× 745 1.1× 617 1.5× 140 0.5× 71 0.3× 108 2.8k
Weimin Zheng China 39 2.5k 2.9× 973 1.4× 1.2k 2.8× 235 0.8× 989 3.7× 413 5.4k
Kalyan Veeramachaneni United States 21 471 0.5× 1.3k 1.9× 363 0.9× 105 0.3× 298 1.1× 82 2.5k

Countries citing papers authored by Qing Liu

Since Specialization
Citations

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

Fields of papers citing papers by Qing Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qing Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Qing Liu. A scholar is included among the top collaborators of Qing Liu 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 Qing Liu. Qing Liu 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.
Eisenhauer, Greg, Norbert Podhorszki, Ana Gainaru, et al.. (2025). HPC I/O innovations in the exascale era. The International Journal of High Performance Computing Applications. 39(4). 594–612. 1 indexed citations
2.
Li, Yanliang, Qian Gong, Qing Liu, et al.. (2025). HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs. 2076–2093.
3.
Gong, Qian, Jieyang Chen, Xin Liang, et al.. (2025). Stability-preserving Lossy Compression for Large-scale Partial Differential Equations. 1992–2005.
4.
Liu, Kai, Guiling Wang, Lili Shan, et al.. (2024). Argon non-thermal plasma treatment promotes the development of rice (Oryza sativa L.) in saline alkali environments. PROTOPLASMA. 261(5). 927–936. 2 indexed citations
5.
Liu, Qing, et al.. (2023). An area-efficient and low-latency elliptic curve scalar multiplication accelerator over prime field. Microprocessors and Microsystems. 103. 104944–104944. 4 indexed citations
6.
Gong, Qian, Jieyang Chen, Ben Whitney, et al.. (2023). MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring. SoftwareX. 24. 101590–101590. 18 indexed citations
7.
Liu, Qing, et al.. (2023). Impermeability and Durability of Self-Compacting Concrete Prepared with Aeolian Sand and Recycled Coarse Aggregate. Materials. 16(23). 7279–7279. 6 indexed citations
8.
9.
Liang, Xin, Ben Whitney, Jieyang Chen, et al.. (2023). Improving Progressive Retrieval for HPC Scientific Data using Deep Neural Network. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 2727–2739. 1 indexed citations
10.
Huang, Dan, et al.. (2022). A Data-driven Approach to Harvesting Latent Reduced Models to Precondition Lossy Compression for Scientific Data. IEEE Transactions on Big Data. 9(3). 949–963. 1 indexed citations
11.
Huang, Dan, et al.. (2022). Identifying challenges and opportunities of in-memory computing on large HPC systems. Journal of Parallel and Distributed Computing. 164. 106–122. 4 indexed citations
12.
Ding, Hao, Qing Liu, & Guangwei Hu. (2022). TDTMF: A recommendation model based on user temporal interest drift and latent review topic evolution with regularization factor. Information Processing & Management. 59(5). 103037–103037. 13 indexed citations
13.
Wang, Yuanmei, et al.. (2021). Critical role of dysfunctional mitochondria and defective mitophagy in autism spectrum disorders. Brain Research Bulletin. 168. 138–145. 14 indexed citations
14.
Su, Kai, et al.. (2020). Strategies of similarity propagation in web service recommender systems. Mathematical Biosciences & Engineering. 18(1). 530–550. 1 indexed citations
15.
Bi, Jing, Haitao Yuan, MengChu Zhou, & Qing Liu. (2019). Time-Dependent Cloud Workload Forecasting via Multi-Task Learning. IEEE Robotics and Automation Letters. 4(3). 2401–2406. 37 indexed citations
16.
Liu, Qing, et al.. (2018). DuoModel: Leveraging Reduced Model for Data Reduction and Re-Computation on HPC Storage. 1(1). 5–8. 3 indexed citations
17.
Yu, Xiang, Qing Liu, Xin Liu, Xiangyang Liu, & Yebao Wang. (2016). A physical-based atmospheric correction algorithm of unmanned aerial vehicles images and its utility analysis. International Journal of Remote Sensing. 38(8-10). 3101–3112. 28 indexed citations
18.
Liu, Qing, et al.. (2012). SHP Component Dynamic Deployment Scheme Based on SCA. Jisuanji gongcheng. 38(7). 227–229. 3 indexed citations
19.
Liu, Qing, et al.. (2008). Web Services Composition with QoS Bound Based on Simulated Annealing Algorithm. Journal of Southeast University. 24(3). 308–311. 5 indexed citations
20.
Han, Yanbo, et al.. (2007). A survey of Web information system and applications. Wuhan University Journal of Natural Sciences. 12(5). 769–772. 1 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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