Fangcheng Fu

908 total citations
39 papers, 345 citations indexed

About

Fangcheng Fu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Hardware and Architecture. According to data from OpenAlex, Fangcheng Fu has authored 39 papers receiving a total of 345 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 17 papers in Computer Vision and Pattern Recognition and 6 papers in Hardware and Architecture. Recurrent topics in Fangcheng Fu's work include Advanced Neural Network Applications (9 papers), Privacy-Preserving Technologies in Data (8 papers) and Parallel Computing and Optimization Techniques (6 papers). Fangcheng Fu is often cited by papers focused on Advanced Neural Network Applications (9 papers), Privacy-Preserving Technologies in Data (8 papers) and Parallel Computing and Optimization Techniques (6 papers). Fangcheng Fu collaborates with scholars based in China, United States and Switzerland. Fangcheng Fu's co-authors include Bin Cui, Jiawei Jiang, Tong Yang, Yingxia Shao, Yangyu Tao, Yong Cheng, Xupeng Miao, Ce Zhang, Lele Yu and Tong Yang and has published in prestigious journals such as Physics Letters B, IEEE Transactions on Knowledge and Data Engineering and Knowledge-Based Systems.

In The Last Decade

Fangcheng Fu

30 papers receiving 335 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fangcheng Fu China 10 250 88 57 41 22 39 345
Pablo Loyola Japan 10 128 0.5× 29 0.3× 61 1.1× 184 4.5× 9 0.4× 30 308
Xiaoyuan Liu United States 8 241 1.0× 12 0.1× 62 1.1× 74 1.8× 7 0.3× 23 342
Toshinori Araki Japan 7 331 1.3× 47 0.5× 98 1.7× 67 1.6× 6 0.3× 19 408
Song Bian China 4 119 0.5× 75 0.9× 34 0.6× 28 0.7× 5 0.2× 4 209
Thomas B. Pedersen Türkiye 9 282 1.1× 54 0.6× 29 0.5× 52 1.3× 6 0.3× 20 336
Renaud Sirdey France 13 346 1.4× 79 0.9× 106 1.9× 119 2.9× 80 3.6× 56 583
Renchi Yang Hong Kong 9 212 0.8× 104 1.2× 67 1.2× 64 1.6× 5 0.2× 26 285
Olivier Markowitch Belgium 9 272 1.1× 89 1.0× 86 1.5× 73 1.8× 107 4.9× 28 330
Sanjukta Bhowmick United States 9 146 0.6× 73 0.8× 100 1.8× 53 1.3× 19 0.9× 49 469
Kazue Sako Japan 12 389 1.6× 68 0.8× 98 1.7× 141 3.4× 14 0.6× 36 459

Countries citing papers authored by Fangcheng Fu

Since Specialization
Citations

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

Fields of papers citing papers by Fangcheng Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fangcheng Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Fangcheng Fu. A scholar is included among the top collaborators of Fangcheng Fu 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 Fangcheng Fu. Fangcheng Fu 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.
Zhang, Hailin, et al.. (2026). Retrieval-Augmented Generation for AI-Generated Content: A Survey. Data Science and Engineering. 11(1). 1–29.
2.
Zhang, Hailin, et al.. (2025). PQCache: Product Quantization-based KVCache for Long Context LLM Inference. Proceedings of the ACM on Management of Data. 3(3). 1–30. 2 indexed citations
3.
Fu, Fangcheng, et al.. (2025). FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism. 421–436. 1 indexed citations
4.
5.
Zhang, Hailin, et al.. (2025). MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training. Proceedings of the ACM on Management of Data. 3(1). 1–28. 1 indexed citations
6.
7.
Fu, Fangcheng, et al.. (2025). Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization. Proceedings of the ACM on Management of Data. 3(3). 1–28. 1 indexed citations
8.
Fu, Fangcheng, et al.. (2024). SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for incomplete multimodal learning in conversational emotion recognition. Knowledge-Based Systems. 309. 112825–112825. 6 indexed citations
9.
Fu, Fangcheng, et al.. (2024). Enabling Parallelism Hot Switching for Efficient Training of Large Language Models. 178–194. 3 indexed citations
10.
Miao, Xupeng, et al.. (2024). Improving Automatic Parallel Training via Balanced Memory Workload Optimization. IEEE Transactions on Knowledge and Data Engineering. 36(8). 3906–3920. 7 indexed citations
11.
Yu, Zihao, Haoyang Li, Fangcheng Fu, Xupeng Miao, & Bin Cui. (2024). Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion Inference. Proceedings of the AAAI Conference on Artificial Intelligence. 38(15). 16605–16613. 4 indexed citations
12.
Wang, Yuxiang, Xiao Yan, Chuang Hu, et al.. (2024). Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning. 3364–3378. 3 indexed citations
13.
Jiang, Jiawei, et al.. (2024). Detecting and Analyzing Motifs in Large-Scale Online Transaction Networks. IEEE Transactions on Knowledge and Data Engineering. 37(2). 584–596.
14.
Fu, Fangcheng, et al.. (2023). OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning. 2142–2150. 4 indexed citations
15.
Fu, Fangcheng, et al.. (2022). BlindFL: Vertical Federated Machine Learning without Peeking into Your Data. Proceedings of the 2022 International Conference on Management of Data. 1316–1330. 39 indexed citations
16.
Jiang, Jiawei, Fangcheng Fu, Tong Yang, Yingxia Shao, & Bin Cui. (2020). SKCompress: compressing sparse and nonuniform gradient in distributed machine learning. The VLDB Journal. 29(5). 945–972. 19 indexed citations
17.
Fu, Fangcheng, et al.. (2018). SketchML Accelerating Distributed Machine Learning with Data Sketches. 1269–1284. 12 indexed citations
18.
Jiang, Jiawei, Bin Cui, Ce Zhang, & Fangcheng Fu. (2018). DimBoost. 1363–1376. 27 indexed citations
19.
Zhao, Maoyuan, Fangcheng Fu, M. Huang, et al.. (2014). Investigation of equation of state and in-mediumNNcross sections through nuclear stopping. Physical Review C. 89(3). 4 indexed citations
20.

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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