Shi Shao-bo

945 total citations · 1 hit paper
9 papers, 562 citations indexed

About

Shi Shao-bo is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, Shi Shao-bo has authored 9 papers receiving a total of 562 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Shi Shao-bo's work include Advanced Neural Network Applications (3 papers), Tensor decomposition and applications (2 papers) and Gas Sensing Nanomaterials and Sensors (2 papers). Shi Shao-bo is often cited by papers focused on Advanced Neural Network Applications (3 papers), Tensor decomposition and applications (2 papers) and Gas Sensing Nanomaterials and Sensors (2 papers). Shi Shao-bo collaborates with scholars based in China. Shi Shao-bo's co-authors include Lei Wang, Tailin Liang, John Glossner, Lan Li, Yaxin Wang, Ximing Chen, Xiaosong Zhang, Yue Qi, Qin Wang and Jianping Xu and has published in prestigious journals such as Physical Chemistry Chemical Physics, Neurocomputing and Chinese Physics Letters.

In The Last Decade

Shi Shao-bo

6 papers receiving 533 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.

Pruning and quantization for deep neural network acceleration: A survey

2021 508 citations Tailin Liang, John Glossner et al. Neurocomputing profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shi Shao-bo China	5	248	236	117	70	40	9	562
Jong Hwan Ko South Korea	16	300 1.2×	156 0.7×	290 2.5×	76 1.1×	35 0.9×	88	702
Yanghan Wang United States	3	694 2.8×	530 2.2×	167 1.4×	61 0.9×	27 0.7×	3	877
Zihan Liu China	12	124 0.5×	137 0.6×	50 0.4×	59 0.8×	63 1.6×	48	416
Cheng-Hung Lin Taiwan	13	137 0.6×	182 0.8×	141 1.2×	130 1.9×	27 0.7×	62	525
Hongxiang Fan United Kingdom	16	284 1.1×	228 1.0×	211 1.8×	35 0.5×	17 0.4×	43	541
Zhiqiang Que United Kingdom	15	238 1.0×	206 0.9×	143 1.2×	29 0.4×	29 0.7×	57	504
Jisu Kim South Korea	13	213 0.9×	78 0.3×	126 1.1×	28 0.4×	17 0.4×	44	448
Xuefei Ning China	13	184 0.7×	281 1.2×	420 3.6×	62 0.9×	32 0.8×	44	737

Countries citing papers authored by Shi Shao-bo

Since Specialization

Citations

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

Fields of papers citing papers by Shi Shao-bo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shi Shao-bo. 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 Shi Shao-bo. The network helps show where Shi Shao-bo may publish in the future.

Co-authorship network of co-authors of Shi Shao-bo

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

9 of 9 papers shown

1.

Shao-bo, Shi, et al.. (2025). Sayram: A Hardware-software Co-design to Accelerate Wireless Baseband Processing. ACM Transactions on Embedded Computing Systems. 25(1). 1–22.

2.

Xu, Jianping, Shi Shao-bo, Jing Chen, et al.. (2023). Improved performance of UV-blue dual-band Bi₂O₃/TiO₂ photodetectors and application of visible light communication with UV light encryption. Physical Chemistry Chemical Physics. 25(44). 30228–30236. 4 indexed citations

3.

Liang, Tailin, et al.. (2022). TCX: A RISC Style Tensor Computing Extension and a Programmable Tensor Processor. ACM Transactions on Embedded Computing Systems. 22(3). 1–27. 1 indexed citations

4.

Liang, Tailin, et al.. (2022). TCX: A Programmable Tensor Processor. 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). 1023–1028.

5.

Liang, Tailin, et al.. (2021). Pruning and quantization for deep neural network acceleration: A survey. Neurocomputing. 461. 370–403. 508 indexed citations breakdown →

6.

Moudgill, Mayan, John Glossner, Wei Huang, et al.. (2020). Heterogeneous Edge CNN Hardware Accelerator. 636–641. 4 indexed citations

7.

Shao-bo, Shi, Yue Qi, & Qin Wang. (2013). FPGA Acceleration for Intersection Computation in Frequent Itemset Mining. 21. 514–519. 4 indexed citations

8.

Shao-bo, Shi, Yue Qi, & Qin Wang. (2013). Accelerating Intersection Computation in Frequent Itemset Mining with FPGA. 21. 659–665. 7 indexed citations

9.

Shao-bo, Shi, et al.. (2010). Effects of Annealing Temperature on Structural and Optical Properties of ZnO Thin Films. Chinese Physics Letters. 27(4). 47803–47803. 34 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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