Carl Yang

701 total citations
12 papers, 304 citations indexed

About

Carl Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Hardware and Architecture. According to data from OpenAlex, Carl Yang has authored 12 papers receiving a total of 304 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 5 papers in Hardware and Architecture. Recurrent topics in Carl Yang's work include Graph Theory and Algorithms (10 papers), Advanced Graph Neural Networks (6 papers) and Parallel Computing and Optimization Techniques (5 papers). Carl Yang is often cited by papers focused on Graph Theory and Algorithms (10 papers), Advanced Graph Neural Networks (6 papers) and Parallel Computing and Optimization Techniques (5 papers). Carl Yang collaborates with scholars based in United States and United Kingdom. Carl Yang's co-authors include John D. Owens, Aydın Buluç, Yangzihao Wang, Yuduo Wu, Yuechao Pan, Scott McMillan, José E. Moreira, Tim Mattson, Andrew Davidson and Leyuan Wang and has published in prestigious journals such as ACM Transactions on Mathematical Software and eScholarship (California Digital Library).

In The Last Decade

Carl Yang

11 papers receiving 294 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Carl Yang United States	10	215	150	137	130	56	12	304
Virat Agarwal United States	9	141 0.7×	145 1.0×	108 0.8×	180 1.4×	53 0.9×	13	317
Farzad Khorasani United States	9	275 1.3×	204 1.4×	163 1.2×	198 1.5×	126 2.3×	11	412
Yuduo Wu United States	6	324 1.5×	183 1.2×	197 1.4×	159 1.2×	119 2.1×	6	416
Yinglong Xia United States	7	182 0.8×	137 0.9×	113 0.8×	156 1.2×	107 1.9×	14	318
Yuechao Pan United States	7	330 1.5×	185 1.2×	199 1.5×	166 1.3×	121 2.2×	7	423
Yangzihao Wang United States	8	346 1.6×	205 1.4×	210 1.5×	174 1.3×	130 2.3×	10	453
Charith Mendis United States	10	96 0.4×	125 0.8×	94 0.7×	73 0.6×	53 0.9×	24	218
Zhijia Zhao United States	12	174 0.8×	284 1.9×	208 1.5×	239 1.8×	107 1.9×	43	498
Xiaowei Zhu China	6	387 1.8×	137 0.9×	238 1.7×	172 1.3×	202 3.6×	21	497
Roman Dementiev Germany	11	72 0.3×	111 0.7×	108 0.8×	184 1.4×	40 0.7×	20	260

Countries citing papers authored by Carl Yang

Since Specialization

Citations

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

Fields of papers citing papers by Carl Yang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Carl Yang

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

12 of 12 papers shown

1.

Cui, Hejie, Nasser Zalmout, Chenwei Zhang, et al.. (2023). PV2TEA: Patching Visual Modality to Textual-Established Information Extraction. 2 indexed citations

2.

Yang, Carl, Aydın Buluç, & John D. Owens. (2022). GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU. ACM Transactions on Mathematical Software. 48(1). 1–51. 34 indexed citations

3.

Cui, Hejie, et al.. (2022). Brain Network Transformer. 25586–25599.

4.

Yang, Carl, et al.. (2019). Graph Coloring on the GPU. 17 indexed citations

5.

Mattson, Tim, Timothy A. Davis, Aydın Buluç, et al.. (2019). LAGraph: A Community Effort to Collect Graph Algorithms Built on Top of the GraphBLAS. 20 indexed citations

6.

Yang, Carl, Aydın Buluç, & John D. Owens. (2018). Implementing Push-Pull Efficiently in GraphBLAS. eScholarship (California Digital Library). 1–11. 20 indexed citations

7.

Mattson, Timothy G., Carl Yang, Scott McMillan, Aydın Buluç, & José E. Moreira. (2017). GraphBLAS C API: Ideas for future versions of the specification. 1–6. 16 indexed citations

8.

Buluç, Aydın, Tim Mattson, Scott McMillan, José E. Moreira, & Carl Yang. (2017). Design of the GraphBLAS API for C. 643–652. 38 indexed citations

9.

Pan, Yuechao, Yangzihao Wang, Yuduo Wu, Carl Yang, & John D. Owens. (2017). Multi-GPU Graph Analytics. 479–490. 34 indexed citations

10.

Wang, Yangzihao, Yuechao Pan, Andrew Davidson, et al.. (2017). Gunrock. 4(1). 1–49. 86 indexed citations

11.

Wu, Yuduo, Yangzihao Wang, Yuechao Pan, Carl Yang, & John D. Owens. (2015). Performance Characterization of High-Level Programming Models for GPU Graph Analytics. 66–75. 15 indexed citations

12.

Yang, Carl, Yangzihao Wang, & John D. Owens. (2015). Fast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU. 22 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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