Lingxiao Ma

862 total citations
16 papers, 349 citations indexed

About

Lingxiao Ma is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Hardware and Architecture. According to data from OpenAlex, Lingxiao Ma has authored 16 papers receiving a total of 349 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 6 papers in Hardware and Architecture. Recurrent topics in Lingxiao Ma's work include Advanced Neural Network Applications (7 papers), Parallel Computing and Optimization Techniques (6 papers) and Graph Theory and Algorithms (6 papers). Lingxiao Ma is often cited by papers focused on Advanced Neural Network Applications (7 papers), Parallel Computing and Optimization Techniques (6 papers) and Graph Theory and Algorithms (6 papers). Lingxiao Ma collaborates with scholars based in China, United Kingdom and United States. Lingxiao Ma's co-authors include Zhi Yang, Jilong Xue, Youshan Miao, Yafei Dai, Lidong Zhou, Lintao Zhang, Bin Cui, Zhi Yang, Ming Wu and Xupeng Miao and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Computer Architecture Letters and Proceedings of the ACM on Management of Data.

In The Last Decade

Lingxiao Ma

14 papers receiving 343 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lingxiao Ma China	11	245	219	73	68	56	16	349
Lingfan Yu China	4	188 0.8×	114 0.5×	29 0.4×	62 0.9×	57 1.0×	6	316
Pengcheng Yao China	11	110 0.4×	165 0.8×	97 1.3×	86 1.3×	74 1.3×	25	273
Oded Padon United States	9	217 0.9×	80 0.4×	101 1.4×	136 2.0×	43 0.8×	28	388
Marat Dukhan United States	7	125 0.5×	147 0.7×	82 1.1×	67 1.0×	49 0.9×	8	266
Dominique LaSalle United States	6	76 0.3×	82 0.4×	61 0.8×	102 1.5×	47 0.8×	7	231
Size Zheng China	8	82 0.3×	115 0.5×	185 2.5×	80 1.2×	85 1.5×	18	287
Gabriel Weisz United States	8	154 0.6×	206 0.9×	249 3.4×	185 2.7×	189 3.4×	11	503
David Lugato France	7	108 0.4×	65 0.3×	200 2.7×	112 1.6×	20 0.4×	15	325
Farzad Khorasani United States	9	163 0.7×	275 1.3×	204 2.8×	198 2.9×	28 0.5×	11	412
Guohao Dai China	9	168 0.7×	272 1.2×	217 3.0×	192 2.8×	154 2.8×	30	469

Countries citing papers authored by Lingxiao Ma

Since Specialization

Citations

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

Fields of papers citing papers by Lingxiao Ma

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lingxiao Ma

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

16 of 16 papers shown

Ma, Lingxiao, et al.. (2025). LUT Tensor Core: A Software-Hardware Co-Design for LUT-Based Low-Bit LLM Inference. 514–528.

Cheng, Yu, et al.. (2025). NeuStream: Bridging Deep Learning Serving and Stream Processing. 671–685.

Cao, Ting, et al.. (2025). T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge. 278–292. 3 indexed citations

Chen, Yuetao, et al.. (2024). ConvStencil: Transform Stencil Computation to Matrix Multiplication on Tensor Cores. 333–347. 10 indexed citations

Miao, Xupeng, et al.. (2023). FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement. Proceedings of the ACM on Management of Data. 1(1). 1–19. 17 indexed citations

Zheng, Ningxin, Huiqiang Jiang, Quanlu Zhang, et al.. (2023). PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation. 331–347. 10 indexed citations

Miao, Xupeng, Yingxia Shao, Zhi Yang, et al.. (2021). Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce. 2262–2270. 46 indexed citations

Hwang, Chang Ho, KyoungSoo Park, Zhiqi Lin, et al.. (2021). Accelerating GNN training with locality-aware partial execution. 34–41. 10 indexed citations

Miao, Xupeng, Lingxiao Ma, Zhi Yang, et al.. (2021). CuWide: Towards Efficient Flow-based Training for Sparse Wide Models on GPUs (Extended Abstract). 2330–2331. 2 indexed citations

10.

Tian, Chao, Lingxiao Ma, Zhi Yang, & Yafei Dai. (2020). PCGCN: Partition-Centric Processing for Accelerating Graph Convolutional Network. 936–945. 26 indexed citations

11.

Ma, Lingxiao, Zhiqiang Xie, Zhi Yang, et al.. (2020). Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks. 881–897. 34 indexed citations

12.

Miao, Xupeng, Lingxiao Ma, Zhi Yang, et al.. (2020). CuWide: Towards Efficient Flow-Based Training for Sparse Wide Models on GPUs. IEEE Transactions on Knowledge and Data Engineering. 34(9). 4119–4132. 9 indexed citations

13.

Zhang, Zhihui, Jingwen Leng, Lingxiao Ma, et al.. (2020). Architectural Implication of Graph Neural Networks. IEEE Computer Architecture Letters. 1–1. 26 indexed citations

14.

Cao, Shi‐Jie, Lingxiao Ma, Wencong Xiao, et al.. (2019). SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization. 11208–11217. 50 indexed citations

15.

Ma, Lingxiao, Zhi Yang, Youshan Miao, et al.. (2019). NeuGraph: Parallel Deep Neural Network Computation on Large Graphs. 443–458. 69 indexed citations

16.

Ma, Lingxiao, Zhi Yang, Han Chen, Jilong Xue, & Yafei Dai. (2017). Garaph: efficient GPU-accelerated graph processing on a single machine with balanced replication. 195–207. 37 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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