Jun Yu
Impact in
- Ecological Modeling top 5%
- Species Distribution and Climate Change
- Marketing top 5%
- Consumer Behavior in Brand Consumption and Identification
- Consumer Retail Behavior Studies
Papers in
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- Species Distribution and Climate Change 7
-
- Image and Signal Denoising Methods 10
- Co-authors
- S. KarlssonMetin AkayJoyce ZhouJi Eun ParkWeng‐Keen WongArun PereiraMark J. ArnoldMikael Karlsson
- Journals
- IEEE Transactions on Biomedical Engineering (3 papers)Signal Processing (3 papers)Magnetic Resonance in Medicine (2 papers)Sustainability (2 papers)Representation Theory of the American Mathematical Society (2 papers)
- Partner nations
- SwedenChinaUnited States
In The Last Decade
Jun Yu
100 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 170
- Ecological Modeling 174
- Marketing 192
- Cognitive Neuroscience 215
- Nature and Landscape Conservation 135
- Information Systems and Management 72
Countries citing papers authored by Jun Yu
This map shows the geographic impact of Jun Yu'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 Jun Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Yu more than expected).
Fields of papers citing papers by Jun Yu
This network shows the impact of papers produced by Jun Yu. 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 Jun Yu. The network helps show where Jun Yu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jun Yu, 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 | 2 | |
| 3 | 2023 | 0 | |
| 4 | 2023 | 0 | |
| 5 | 2023 | 5 | |
| 6 | 2023 | 6 | |
| 7 | 2022 | 23 | |
| 8 | 2021 | 9 | |
| 9 | 2021 | 3 | |
| 10 | 2020 | 21 | |
| 11 | 2020 | 2 | |
| 12 | 2019 | 2 | |
| 13 | 2018 | 3 | |
| 14 | 2018 | 18 | |
| 15 | 2017 | 1 | |
| 16 | 2015 | 11 | |
| 17 | 2015 | 2 | |
| 18 | 2014 | 22 | |
| 19 | Effect of Teaching Method on Students’ Perceptions of Instructor Attributes | 2012 | 4 |
| 20 | Context and Text Interpretation in Listening English | 2006 | 1 |
About Jun Yu
Jun Yu is a scholar working on Ecological Modeling, Computer Vision and Pattern Recognition, Marketing, Signal Processing and Statistics and Probability, having authored 115 papers that have together received 1.8k indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (10 papers), Sparse and Compressive Sensing Techniques (9 papers), Blind Source Separation Techniques (8 papers), Species Distribution and Climate Change (7 papers), Muscle activation and electromyography studies (6 papers), Digital Marketing and Social Media (6 papers), Advanced MRI Techniques and Applications (6 papers) and Tree-ring climate responses (4 papers). The work is most often cited by research in Ecological Modeling (174 citations), Marketing (192 citations), Cognitive Neuroscience (215 citations), Nature and Landscape Conservation (135 citations) and Information Systems and Management (72 citations). Jun Yu has collaborated with scholars based in Sweden, China and United States. Frequent co-authors include S. Karlsson, Metin Akay, Joyce Zhou, Ji Eun Park, Weng‐Keen Wong, Arun Pereira, Mark J. Arnold, Mikael Karlsson, Thomas Asklund and Steve Kelling. Their work appears in journals such as IEEE Transactions on Biomedical Engineering, Signal Processing, Magnetic Resonance in Medicine, Sustainability and Representation Theory of the American Mathematical Society.
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.