Xiaohan Wei
- Signal Processing top 5%
- Blind Source Separation Techniques 3
- Electrochemistry top 10%
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- Advanced Wireless Network Optimization 7
- Electrochemical sensors and biosensors 3
- Computational Mechanics top 10%
- Sparse and Compressive Sensing Techniques 10
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- Stochastic Gradient Optimization Techniques 6
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- Advanced Bandit Algorithms Research 6
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- Machine Fault Diagnosis Techniques 5
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- Gear and Bearing Dynamics Analysis 3
- Partner nations
- ChinaUnited StatesSwitzerland
In The Last Decade
Xiaohan Wei
47 papers receiving 582 citations
Peers
Comparison fields: 5 of 96
- Signal Processing 153
- Electrochemistry 54
- Ceramics and Composites 41
- Electrical and Electronic Engineering 249
- Computational Mechanics 83
Countries citing papers authored by Xiaohan Wei
This map shows the geographic impact of Xiaohan Wei'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 Xiaohan Wei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaohan Wei more than expected).
Fields of papers citing papers by Xiaohan Wei
This network shows the impact of papers produced by Xiaohan Wei. 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 Xiaohan Wei. The network helps show where Xiaohan Wei may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Xiaohan Wei, 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 | 3 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 16 | |
| 4 | 2024 | 0 | |
| 5 | 2023 | 7 | |
| 6 | 2022 | 9 | |
| 7 | 2022 | 3 | |
| 8 | 2021 | 5 | |
| 9 | 2021 | 4 | |
| 10 | 2021 | 0 | |
| 11 | 2021 | 1 | |
| 12 | Robust One-Bit Recovery via ReLU Generative Networks: Near-Optimal Statistical Rate and Global Landscape Analysis | 2020 | 1 |
| 13 | Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism. | 2020 | 4 |
| 14 | 2020 | 8 | |
| 15 | On the statistical rate of nonlinear recovery in generative models with heavy-tailed data | 2019 | 8 |
| 16 | 2018 | 1 | |
| 17 | 2015 | 6 | |
| 18 | 2010 | 31 | |
| 19 | 2002 | 17 | |
| 20 | 1994 | 101 |
About Xiaohan Wei
Xiaohan Wei is a scholar working on Acoustics and Ultrasonics, Computational Mechanics, Management Science and Operations Research, Computer Networks and Communications and Signal Processing, having authored 50 papers that have together received 592 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (10 papers), Advanced Wireless Network Optimization (7 papers), Stochastic Gradient Optimization Techniques (6 papers), Advanced Bandit Algorithms Research (6 papers), Machine Fault Diagnosis Techniques (5 papers), Gear and Bearing Dynamics Analysis (3 papers), Blind Source Separation Techniques (3 papers) and Electrochemical sensors and biosensors (3 papers). The work is most often cited by research in Signal Processing (153 citations), Electrochemistry (54 citations), Ceramics and Composites (41 citations), Electrical and Electronic Engineering (249 citations) and Computational Mechanics (83 citations). Xiaohan Wei has collaborated with scholars based in China, United States and Switzerland. Frequent co-authors include Zhongfu Ye, Xu Xu, Peter K. Davies, R. Christoffersen, T. Negas, Michael J. Neely, Qing Ling, Fei Wang, Baoxian Ye and Hao Yu. Their work appears in journals such as IEEE/ACM Transactions on Networking, Measurement, Bernoulli, IEEE Micro and Journal of Electroanalytical Chemistry.
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.