Chen-Yu Ho
Impact in
- Artificial Intelligence top 10%
- Privacy-Preserving Technologies in Data
- Stochastic Gradient Optimization Techniques
- Cryptography and Data Security
- Domain Adaptation and Few-Shot Learning
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- IoT and Edge/Fog Computing
- Software-Defined Networks and 5G
Papers in
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- Stochastic Gradient Optimization Techniques 3
- Privacy-Preserving Technologies in Data 2
- Cryptography and Data Security 2
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- Energy Efficient Wireless Sensor Networks 2
- IoT and Edge/Fog Computing 1
- Co-authors
- Marco Canini (5 shared papers)Ahmed M. Abdelmoniem (4 shared papers)Amedeo Sapio (1 shared paper)El Houcine Bergou (2 shared papers)Panos Kalnis (2 shared papers)Hang Xu (2 shared papers)Po-Hung Chen (1 shared paper)
- Journals
- IEEE Internet of Things Journal (1 paper)King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (3 papers)Queen Mary Research Online (Queen Mary University of London) (1 paper)
- Partner nations
- Saudi ArabiaUnited KingdomCanada
In The Last Decade
Chen-Yu Ho
6 papers receiving 193 citations
Peers
Comparison fields: 5 of 32
- Artificial Intelligence 130
- Computer Networks and Communications 65
- Computer Vision and Pattern Recognition 46
- Health Informatics 3
- Hardware and Architecture 14
Countries citing papers authored by Chen-Yu Ho
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
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-authors
The 7 scholars most cited alongside Chen-Yu Ho, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 60 | |
| 2 | 2023 | 51 | |
| 3 | 2021 | 40 | |
| 4 | Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation | 2020 | 22 |
| 5 | 2022 | 19 | |
| 6 | 2019 | 5 |
About Chen-Yu Ho
Chen-Yu Ho is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Computational Mechanics and Neurology, having authored 6 papers that have together received 197 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (3 papers), Privacy-Preserving Technologies in Data (2 papers), Energy Efficient Wireless Sensor Networks (2 papers), Cryptography and Data Security (2 papers), IoT and Edge/Fog Computing (1 paper), Wireless Body Area Networks (1 paper), Brain Tumor Detection and Classification (1 paper) and Sparse and Compressive Sensing Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (130 citations), Computer Networks and Communications (65 citations), Computer Vision and Pattern Recognition (46 citations), Health Informatics (3 citations) and Hardware and Architecture (14 citations). Chen-Yu Ho has collaborated with scholars based in Saudi Arabia, United Kingdom and Canada. Frequent co-authors include Marco Canini, Ahmed M. Abdelmoniem, Amedeo Sapio, El Houcine Bergou, Panos Kalnis, Hang Xu and Po-Hung Chen. Their work appears in 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).
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.