Chen-Yu Ho

522 total citations
6 papers, 197 citations indexed

About

Chen-Yu Ho is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Chen-Yu Ho has authored 6 papers receiving a total of 197 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 2 papers in Computer Networks and Communications and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Chen-Yu Ho's work include Stochastic Gradient Optimization Techniques (3 papers), Cryptography and Data Security (2 papers) and Privacy-Preserving Technologies in Data (2 papers). Chen-Yu Ho is often cited by papers focused on Stochastic Gradient Optimization Techniques (3 papers), Cryptography and Data Security (2 papers) and Privacy-Preserving Technologies in Data (2 papers). Chen-Yu Ho collaborates with scholars based in Saudi Arabia, United Kingdom and Canada. Chen-Yu Ho's co-authors include Marco Canini, Ahmed M. Abdelmoniem, Amedeo Sapio, Panos Kalnis, El Houcine Bergou, Hang Xu and Po-Hung Chen and has published in prestigious journals such as IEEE Internet of Things Journal, King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) and Queen Mary Research Online (Queen Mary University of London).

In The Last Decade

Chen-Yu Ho

6 papers receiving 193 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chen-Yu Ho Saudi Arabia	6	130	65	46	33	19	6	197
Chenhao Xie China	6	131 1.0×	15 0.2×	57 1.2×	46 1.4×	30 1.6×	14	180
Dong-Jun Han South Korea	9	138 1.1×	87 1.3×	22 0.5×	76 2.3×	26 1.4×	37	237
Quanlu Zhang China	10	77 0.6×	147 2.3×	48 1.0×	14 0.4×	120 6.3×	18	235
Mohammad Samragh United States	9	170 1.3×	26 0.4×	56 1.2×	116 3.5×	12 0.6×	23	264
Nishant Kumar India	4	154 1.2×	10 0.2×	17 0.4×	16 0.5×	26 1.4×	8	184
Vadim Sheinin United States	7	95 0.7×	27 0.4×	63 1.4×	24 0.7×	33 1.7×	28	174
Benoit Steiner United States	4	83 0.6×	61 0.9×	105 2.3×	37 1.1×	34 1.8×	5	213
S. Sree Vivek India	8	147 1.1×	30 0.5×	52 1.1×	11 0.3×	62 3.3×	36	182
Osama Hosam Saudi Arabia	8	54 0.4×	58 0.9×	90 2.0×	19 0.6×	88 4.6×	28	209
Shangqing Zhao United States	8	158 1.2×	79 1.2×	22 0.5×	67 2.0×	25 1.3×	26	247

Countries citing papers authored by Chen-Yu Ho

Since Specialization

Citations

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

Fields of papers citing papers by Chen-Yu Ho

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chen-Yu Ho. 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 Chen-Yu Ho. The network helps show where Chen-Yu Ho may publish in the future.

Co-authorship network of co-authors of Chen-Yu Ho

This figure shows the co-authorship network connecting the top 25 collaborators of Chen-Yu Ho. A scholar is included among the top collaborators of Chen-Yu Ho 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 Chen-Yu Ho. Chen-Yu Ho is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

1.

Abdelmoniem, Ahmed M., et al.. (2023). A Comprehensive Empirical Study of Heterogeneity in Federated Learning. IEEE Internet of Things Journal. 10(16). 14071–14083. 51 indexed citations

2.

Abdelmoniem, Ahmed M., et al.. (2022). Empirical analysis of federated learning in heterogeneous environments. Queen Mary Research Online (Queen Mary University of London). 1–9. 19 indexed citations

3.

Ho, Chen-Yu, et al.. (2021). Efficient sparse collective communication and its application to accelerate distributed deep learning. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 676–691. 60 indexed citations

4.

Xu, Hang, Chen-Yu Ho, Ahmed M. Abdelmoniem, et al.. (2021). GRACE: A Compressed Communication Framework for Distributed Machine Learning. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 561–572. 40 indexed citations

5.

Xu, Hang, Chen-Yu Ho, Ahmed M. Abdelmoniem, et al.. (2020). Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 22 indexed citations

6.

Chen, Po-Hung, et al.. (2019). The Implementation of Wireless Industrial Internet of Things (IIoT) Based Upon IEEE 802.15.4-2015 TSCH Access Mode. 367–369. 5 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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