James Cheng
- Computational Mathematics top 0.5%
- Signal Processing top 0.5%
- Data Management and Algorithms 30
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- Graph Theory and Algorithms 48
- Statistical and Nonlinear Physics top 0.5%
- Complex Network Analysis Techniques 19
- Artificial Intelligence top 0.5%
- Advanced Graph Neural Networks 31
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- Cloud Computing and Resource Management 22
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- Sparse and Compressive Sensing Techniques 21
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- Advanced Database Systems and Queries 16
- Caching and Content Delivery 13
James Cheng
132 papers receiving 4.6k citations
Peers
Comparison fields: 5 of 109
- Computational Mathematics 227
- Signal Processing 1.2k
- Computer Vision and Pattern Recognition 2.1k
- Statistical and Nonlinear Physics 1.2k
- Artificial Intelligence 2.2k
Countries citing papers authored by James Cheng
This map shows the geographic impact of James Cheng'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 James Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Cheng more than expected).
Fields of papers citing papers by James Cheng
This network shows the impact of papers produced by James Cheng. 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 James Cheng. The network helps show where James Cheng may publish in the future.
Co-authorship network
The 25 scholars most cited alongside James Cheng, 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 | 2024 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 5 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 12 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 6 | |
| 8 | 2022 | 11 | |
| 9 | 2022 | 8 | |
| 10 | 2021 | 24 | |
| 11 | Scaling Large Production Clusters with Partitioned Synchronization | 2021 | 4 |
| 12 | 2021 | 32 | |
| 13 | Tangram: Bridging Immutable and Mutable Abstractions for Distributed Data Analytics | 2019 | 4 |
| 14 | Direct Acceleration of SAGA using Sampled Negative Momentum | 2019 | 7 |
| 15 | 2018 | 43 | |
| 16 | 2018 | 4 | |
| 17 | Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization | 2018 | 2 |
| 18 | 2017 | 133 | |
| 19 | 2017 | 25 | |
| 20 | 2015 | 51 |
About James Cheng
James Cheng is a scholar working on Computational Mathematics, Signal Processing, Computer Vision and Pattern Recognition, Computer Networks and Communications and Artificial Intelligence, having authored 137 papers that have together received 4.7k indexed citations. Recurring topics across this work include Graph Theory and Algorithms (48 papers), Advanced Graph Neural Networks (31 papers), Data Management and Algorithms (30 papers), Cloud Computing and Resource Management (22 papers), Sparse and Compressive Sensing Techniques (21 papers), Complex Network Analysis Techniques (19 papers), Advanced Database Systems and Queries (16 papers) and Caching and Content Delivery (13 papers). The work is most often cited by research in Computational Mathematics (227 citations), Signal Processing (1.2k citations), Computer Vision and Pattern Recognition (2.1k citations), Statistical and Nonlinear Physics (1.2k citations) and Artificial Intelligence (2.2k citations). James Cheng has collaborated with scholars based in Hong Kong, China and Singapore. Frequent co-authors include Yiping Ke, Wilfred Ng, Ada Wai-Chee Fu, Shumo Chu, Da Yan, Fanhua Shang, Jia Wang, Yi Lu, Huanhuan Wu and Yuanyuan Liu. Their work appears in journals such as Proceedings of the VLDB Endowment, Knowledge and Information Systems, IEEE Transactions on Parallel and Distributed Systems, ACM Transactions on Knowledge Discovery from Data and ACM Transactions on Database Systems.
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.