Shengen Yan

1.9k total citations
32 papers, 946 citations indexed

About

Shengen Yan is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Hardware and Architecture. According to data from OpenAlex, Shengen Yan has authored 32 papers receiving a total of 946 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 11 papers in Computer Networks and Communications and 11 papers in Hardware and Architecture. Recurrent topics in Shengen Yan's work include Advanced Neural Network Applications (16 papers), Parallel Computing and Optimization Techniques (11 papers) and Advanced Data Storage Technologies (9 papers). Shengen Yan is often cited by papers focused on Advanced Neural Network Applications (16 papers), Parallel Computing and Optimization Techniques (11 papers) and Advanced Data Storage Technologies (9 papers). Shengen Yan collaborates with scholars based in China, Hong Kong and United States. Shengen Yan's co-authors include Yun Liang, Liqiang Lu, Qingcheng Xiao, Yunquan Zhang, Huiyang Zhou, Chao Li, Yu‐Wing Tai, Guoping Long, Yonggang Wen and Peng Sun and has published in prestigious journals such as IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

In The Last Decade

Shengen Yan

29 papers receiving 924 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shengen Yan China	14	495	371	332	285	252	32	946
Tong Geng United States	18	368 0.7×	289 0.8×	339 1.0×	213 0.7×	398 1.6×	70	951
Debbie Marr United States	12	471 1.0×	298 0.8×	483 1.5×	191 0.7×	350 1.4×	17	989
Yuwei Hu United States	10	463 0.9×	342 0.9×	231 0.7×	213 0.7×	341 1.4×	12	858
Hardik Sharma United States	6	493 1.0×	280 0.8×	518 1.6×	139 0.5×	362 1.4×	13	918
Hanrui Wang United States	15	456 0.9×	301 0.8×	482 1.5×	205 0.7×	623 2.5×	43	1.3k
Jorge Albericio Canada	12	677 1.4×	433 1.2×	664 2.0×	237 0.8×	414 1.6×	20	1.2k
Kalin Ovtcharov Canada	10	367 0.7×	481 1.3×	406 1.2×	420 1.5×	270 1.1×	14	1.1k
Jongse Park United States	18	581 1.2×	539 1.5×	835 2.5×	351 1.2×	516 2.0×	43	1.5k
Anurag Mukkara United States	8	844 1.7×	533 1.4×	680 2.0×	301 1.1×	512 2.0×	9	1.4k
Amir Yazdanbakhsh United States	18	323 0.7×	587 1.6×	725 2.2×	304 1.1×	314 1.2×	51	1.2k

Countries citing papers authored by Shengen Yan

Since Specialization

Citations

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

Fields of papers citing papers by Shengen Yan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shengen Yan

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

20 of 20 papers shown

1.

Ning, Xuefei, Xihui Liu, Xiuhong Li, et al.. (2025). MBQ: Modality-Balanced Quantization for Large Vision-Language Models. 4167–4177.

2.

Yan, Shengen, et al.. (2024). Proteus: Simulating the Performance of Distributed DNN Training. IEEE Transactions on Parallel and Distributed Systems. 35(10). 1867–1878. 2 indexed citations

3.

Dai, Guohao, Xuefei Ning, Yu Wang, et al.. (2024). DiTFastAttn: Attention Compression for Diffusion Transformer Models. 1196–1219.

4.

Zheng, Size, Siyuan Chen, Xiuhong Li, et al.. (2023). Chimera: An Analytical Optimizing Framework for Effective Compute-intensive Operators Fusion. 1113–1126. 21 indexed citations

5.

Li, Xiuhong, et al.. (2022). LongTail-Bench: A Benchmark Suite for Domain-Specific Operators in Deep Learning. 282–295. 1 indexed citations

6.

Zheng, Size, et al.. (2022). AMOS. 874–887. 37 indexed citations

7.

Luo, Qiong, et al.. (2021). DIESEL+: Accelerating Distributed Deep Learning Tasks on Image Datasets. IEEE Transactions on Parallel and Distributed Systems. 33(5). 1173–1184. 5 indexed citations

8.

Sun, Peng, Wei Gao, Tianwei Zhang, et al.. (2021). ASTRAEA: A Fair Deep Learning Scheduler for Multi-Tenant GPU Clusters. IEEE Transactions on Parallel and Distributed Systems. 33(11). 2781–2793. 15 indexed citations

9.

Liang, Yun, et al.. (2020). Enabling Efficient Fast Convolution Algorithms on GPUs via MegaKernels. IEEE Transactions on Computers. 1–1. 10 indexed citations

10.

Xie, Lei, Jidong Zhai, Baodong Wu, et al.. (2020). Elan: Towards Generic and Efficient Elastic Training for Deep Learning. 78–88. 13 indexed citations

11.

Liang, Yun, et al.. (2019). A coordinated tiling and batching framework for efficient GEMM on GPUs. 229–241. 47 indexed citations

12.

Lu, Liqiang, Yun Liang, Qingcheng Xiao, & Shengen Yan. (2017). Evaluating Fast Algorithms for Convolutional Neural Networks on FPGAs. 101–108. 193 indexed citations

13.

Xiao, Qingcheng, Yun Liang, Liqiang Lu, Shengen Yan, & Yu‐Wing Tai. (2017). Exploring Heterogeneous Algorithms for Accelerating Deep Convolutional Neural Networks on FPGAs. 1–6. 153 indexed citations

14.

Dang, Qingqing, et al.. (2014). A fast integral image generation algorithm on GPUs. 624–631. 3 indexed citations

15.

Yan, Shengen, Chao Li, Yunquan Zhang, & Huiyang Zhou. (2014). yaSpMV. 107–118. 101 indexed citations

16.

Yan, Shengen, Guoping Long, & Yunquan Zhang. (2013). StreamScan. 229–238. 60 indexed citations

17.

Yan, Shengen, Guoping Long, & Yunquan Zhang. (2013). StreamScan. ACM SIGPLAN Notices. 48(8). 229–238. 8 indexed citations

18.

Wang, Weiyan, et al.. (2013). CLSIFT: An Optimization Study of the Scale Invariance Feature Transform on GPUs. 93–100. 6 indexed citations

19.

Yan, Shengen, Yunquan Zhang, & Guoping Long. (2012). Summed-area table algorithm optimization based on the OpenCL. IEEE International Conference on High Performance Computing, Data, and Analytics. 41. 1 indexed citations

20.

Wang, Weiyan, et al.. (2012). Parallelization and performance optimization on face detection algorithm with OpenCL: A case study. Tsinghua Science & Technology. 17(3). 287–295. 17 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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