Hanpeng Liu
- Computational Mathematics top 5%
- Tensor decomposition and applications 1
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- IoT and Edge/Fog Computing 1
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- CCD and CMOS Imaging Sensors 1
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- Machine Learning in Healthcare 1
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- Advanced Neural Network Applications 2
- Graph Theory and Algorithms 1
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- Sparse and Compressive Sensing Techniques 1
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- Cloud Computing and Resource Management 1
- Co-authors
- Bhaskar KrishnamachariPedro Henrique GomesShangxing WangYan LiuMichael TsangYaguang LiPavankumar MuraliSanjay Purushotham
- Cited by
- Computational MathematicsComputer Networks and CommunicationsElectrical and Electronic Engineering
- Journals
- IEEE Transactions on Cognitive Communications and Networking (1 paper)IEEE Transactions on Emerging Topics in Computational Intelligence (1 paper)Big Data Research (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Hanpeng Liu
7 papers receiving 429 citations
Hit Papers
Peers
Comparison fields: 5 of 57
- Computational Mathematics 34
- Computer Networks and Communications 250
- Electrical and Electronic Engineering 259
- Artificial Intelligence 84
- Management Science and Operations Research 30
Countries citing papers authored by Hanpeng Liu
This map shows the geographic impact of Hanpeng Liu'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 Hanpeng Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hanpeng Liu more than expected).
Fields of papers citing papers by Hanpeng Liu
This network shows the impact of papers produced by Hanpeng Liu. 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 Hanpeng Liu. The network helps show where Hanpeng Liu may publish in the future.
Co-authorship network
The 15 scholars most cited alongside Hanpeng Liu, 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 | 2025 | 2 | |
| 2 | 2024 | 2 | |
| 3 | 2024 | 5 | |
| 4 | 2023 | 0 | |
| 5 | 2020 | 24 | |
| 6 | 2019 | 50 | |
| 7 | Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability | 2018 | 15 |
| 8 | Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networksbreakdown → | 2018 | 341 |
About Hanpeng Liu
Hanpeng Liu is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Marketing, having authored 8 papers that have together received 439 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (2 papers), IoT and Edge/Fog Computing (1 paper), Sparse and Compressive Sensing Techniques (1 paper), Tensor decomposition and applications (1 paper), CCD and CMOS Imaging Sensors (1 paper), Machine Learning in Healthcare (1 paper), Cloud Computing and Resource Management (1 paper) and Graph Theory and Algorithms (1 paper). The work is most often cited by research in Computational Mathematics (34 citations), Computer Networks and Communications (250 citations) and Electrical and Electronic Engineering (259 citations). Hanpeng Liu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Bhaskar Krishnamachari, Pedro Henrique Gomes, Shangxing Wang, Yan Liu, Michael Tsang, Yaguang Li, Pavankumar Murali, Sanjay Purushotham, Weiran Shen and Pingzhong Tang. Their work appears in journals such as IEEE Transactions on Cognitive Communications and Networking, IEEE Transactions on Emerging Topics in Computational Intelligence and Big Data Research.
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.