Boyu Wang
- Cognitive Neuroscience top 5%
- EEG and Brain-Computer Interfaces 18
- Neural dynamics and brain function 9
- Signal Processing top 5%
- Blind Source Separation Techniques 7
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- Neuroscience and Neural Engineering 6
- Human-Computer Interaction top 5%
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning 21
- Machine Learning and ELM 5
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- Multimodal Machine Learning Applications 10
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- Advanced Memory and Neural Computing 4
Boyu Wang
65 papers receiving 927 citations
Peers
Comparison fields: 5 of 114
- Cognitive Neuroscience 486
- Signal Processing 143
- Cellular and Molecular Neuroscience 234
- Human-Computer Interaction 70
- Artificial Intelligence 300
Countries citing papers authored by Boyu Wang
This map shows the geographic impact of Boyu Wang'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 Boyu Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Boyu Wang more than expected).
Fields of papers citing papers by Boyu Wang
This network shows the impact of papers produced by Boyu Wang. 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 Boyu Wang. The network helps show where Boyu Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Boyu Wang, 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 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 3 | |
| 5 | 2024 | 2 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 0 | |
| 8 | 2024 | 8 | |
| 9 | 2023 | 1 | |
| 10 | 2023 | 8 | |
| 11 | 2023 | 2 | |
| 12 | 2023 | 2 | |
| 13 | 2023 | 1 | |
| 14 | 2023 | 8 | |
| 15 | 2021 | 2 | |
| 16 | 2020 | 15 | |
| 17 | Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting | 2020 | 1 |
| 18 | Transfer Learning via Minimizing the Performance Gap Between Domains | 2019 | 18 |
| 19 | 2018 | 18 | |
| 20 | 2010 | 5 |
About Boyu Wang
Boyu Wang is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Neuropsychology and Physiological Psychology and Signal Processing, having authored 69 papers that have together received 951 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (21 papers), EEG and Brain-Computer Interfaces (18 papers), Multimodal Machine Learning Applications (10 papers), Neural dynamics and brain function (9 papers), Blind Source Separation Techniques (7 papers), Neuroscience and Neural Engineering (6 papers), Machine Learning and ELM (5 papers) and Advanced Memory and Neural Computing (4 papers). The work is most often cited by research in Cognitive Neuroscience (486 citations), Signal Processing (143 citations), Cellular and Molecular Neuroscience (234 citations), Human-Computer Interaction (70 citations) and Artificial Intelligence (300 citations). Boyu Wang has collaborated with scholars based in Canada, China and Macao. Frequent co-authors include Feng Wan, Chi Man Wong, Alessandro Pasquale De Rosa, Peng Un Mak, Mang I Vai, Ze Wang, Changjian Shui, Fan Zhou, Pui‐In Mak and Wenya Nan. Their work appears in journals such as Knowledge-Based Systems, Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, European Heart Journal - Quality of Care and Clinical Outcomes and Scientific Reports.
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.