Minghai Qin

1.4k total citations · 1 hit paper
42 papers, 709 citations indexed

About

Minghai Qin is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Minghai Qin has authored 42 papers receiving a total of 709 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Networks and Communications, 16 papers in Electrical and Electronic Engineering and 15 papers in Computer Vision and Pattern Recognition. Recurrent topics in Minghai Qin's work include Cellular Automata and Applications (15 papers), Advanced Data Storage Technologies (13 papers) and Error Correcting Code Techniques (11 papers). Minghai Qin is often cited by papers focused on Cellular Automata and Applications (15 papers), Advanced Data Storage Technologies (13 papers) and Error Correcting Code Techniques (11 papers). Minghai Qin collaborates with scholars based in United States, Mexico and Japan. Minghai Qin's co-authors include Fei Sun, Paul H. Siegel, Fengbo Ren, Yuhao Wang, Kai Xu, Albert Guillén i Fàbregas, Eitan Yaakobi, Borja Peleato, A. K. Bhatia and Rajiv Agarwal and has published in prestigious journals such as IEEE Transactions on Information Theory, IEEE Journal on Selected Areas in Communications and IEEE Transactions on Communications.

In The Last Decade

Minghai Qin

38 papers receiving 694 citations

Hit Papers

Learning in the Frequency Domain 2020 2026 2022 2024 2020 100 200 300
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. Learning in the Frequency Domain
    2020 309 citations Kai Xu, Minghai Qin 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
Minghai Qin United States 11 293 236 216 173 86 42 709
Yoshiki Yamaguchi Japan 11 130 0.4× 180 0.8× 141 0.7× 154 0.9× 28 0.3× 77 570
Manuel Ujaldón Spain 13 193 0.7× 165 0.7× 185 0.9× 44 0.3× 93 1.1× 47 581
T. Radhika India 12 110 0.4× 203 0.9× 204 0.9× 106 0.6× 42 0.5× 24 555
Nan Zhou China 14 510 1.7× 103 0.4× 244 1.1× 53 0.3× 81 0.9× 47 801
Xinyu Niu United Kingdom 18 344 1.2× 80 0.3× 250 1.2× 288 1.7× 34 0.4× 71 818
Csaba Rekeczky Hungary 13 122 0.4× 274 1.2× 196 0.9× 216 1.2× 99 1.2× 54 519
Li Shang China 14 337 1.2× 203 0.9× 149 0.7× 373 2.2× 40 0.5× 54 1.0k
Wei Ding China 14 281 1.0× 301 1.3× 112 0.5× 106 0.6× 31 0.4× 83 807
Rodolphe Jenatton France 14 418 1.4× 72 0.3× 353 1.6× 61 0.4× 64 0.7× 19 1.1k
Chi-Jen Lu Taiwan 12 448 1.5× 69 0.3× 291 1.3× 70 0.4× 152 1.8× 39 794

Countries citing papers authored by Minghai Qin

Since Specialization
Citations

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

Fields of papers citing papers by Minghai Qin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Minghai Qin

This figure shows the co-authorship network connecting the top 25 collaborators of Minghai Qin. A scholar is included among the top collaborators of Minghai Qin 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 Minghai Qin. Minghai Qin 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.
2.
Qin, Minghai, et al.. (2024). A Min-Max Optimization Framework for Multi-task Deep Neural Network Compression. 1–5. 1 indexed citations
3.
Kong, Zhenglun, Haoyu Ma, Geng Yuan, et al.. (2023). Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training. Proceedings of the AAAI Conference on Artificial Intelligence. 37(7). 8360–8368. 10 indexed citations
4.
Li, Yanyu, Pu Zhao, Geng Yuan, et al.. (2023). Towards Real-Time Segmentation on the Edge. Proceedings of the AAAI Conference on Artificial Intelligence. 37(2). 1468–1476. 6 indexed citations
5.
Kong, Zhenglun, Minghai Qin, Peiyan Dong, et al.. (2023). Data Level Lottery Ticket Hypothesis for Vision Transformers. 1378–1386. 3 indexed citations
6.
Li, Gen, Jie Ji, Minghai Qin, et al.. (2023). Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting. 10259–10269. 10 indexed citations
7.
Yuan, Geng, Alec Lu, Mengshu Sun, et al.. (2023). ESRU: Extremely Low-Bit and Hardware-Efficient Stochastic Rounding Unit Design for Low-Bit DNN Training. 1–6. 3 indexed citations
8.
Huang, Guyue, Haoran Li, Minghai Qin, et al.. (2022). Shfl-BW. Proceedings of the 59th ACM/IEEE Design Automation Conference. 1153–1158. 9 indexed citations
9.
Kong, Zhenglun, Peiyan Dong, Xiaolong Ma, et al.. (2021). HFSP: A Hardware-friendly Soft Pruning Framework for Vision Transformers. 1 indexed citations
10.
Xu, Kai, et al.. (2020). Learning in the Frequency Domain. 1737–1746. 309 indexed citations breakdown →
11.
Qin, Minghai, et al.. (2019). Garbage Collection Algorithms for Meta Data Updates in NAND Flash. 30. 1–5. 1 indexed citations
12.
Qin, Minghai. (2018). Hamming-Distance-Based Binary Representation of Numbers. 25. 2202–2205. 1 indexed citations
13.
Qin, Minghai, et al.. (2018). Improving Robustness of Neural Networks against Bit Flipping Errors during Inference. Journal of Image and Graphics. 6(2). 181–186. 5 indexed citations
14.
Fan, Bing, Minghai Qin, & Paul H. Siegel. (2017). Enhancing the Expected Lifetime of NAND Flash by Short <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math> </inline-formula>-Ary WOM Codes. IEEE Communications Letters. 22(7). 1302–1305.
15.
Qin, Minghai, et al.. (2017). Low Read Latency Rewriting Codes for Multi-Level 3-D NAND Flash. IEEE Communications Letters. 21(7). 1477–1480. 3 indexed citations
16.
Qin, Minghai. (2017). Fractional Bits-Per-Cell for NAND Flash with Low Read Latency. 1–6. 1 indexed citations
17.
Qin, Minghai, Robert Mateescu, Cyril Guyot, & Zvonimir Bandić. (2015). Balanced codes for data retention of multi-level flash memories with fast page read. 704–711. 3 indexed citations
18.
Qin, Minghai, Eitan Yaakobi, & Paul H. Siegel. (2014). Optimized Cell Programming for Flash Memories With Quantizers. IEEE Transactions on Information Theory. 60(5). 2780–2795. 3 indexed citations
19.
Qin, Minghai, Anxiao Jiang, & Paul H. Siegel. (2013). Parallel programming of rank modulation. 6. 719–723. 6 indexed citations
20.
Peleato, Borja, Rajiv Agarwal, J.M. Cioffi, Minghai Qin, & Paul H. Siegel. (2012). Towards minimizing read time for NAND flash. 3219–3224. 19 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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