Jay Li
Impact in
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- Cardiac Imaging and Diagnostics
- Radiation Dose and Imaging
- Advanced MRI Techniques and Applications
- Medical Imaging Techniques and Applications
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- Neurological disorders and treatments
Papers in ⓘ
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- Neurological disorders and treatments 5
- Botulinum Toxin and Related Neurological Disorders 2
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- Genetic Neurodegenerative Diseases 4
- Co-authors
- Jiang Hsieh (2 shared papers)Melissa Vass (1 shared paper)Darin Okerlund (1 shared paper)Xiangyang Tang (1 shared paper)William T. Dauer (5 shared papers)Samuel S. Pappas (4 shared papers)Irina Grishina (2 shared papers)Sumathi Sankaran‐Walters (2 shared papers)
- Journals
- eLife (2 papers)ACS Chemical Neuroscience (1 paper)Medical Physics (1 paper)Electroanalysis (1 paper)Journal of Clinical Investigation (1 paper)
- Partner nations
- United StatesTaiwanBrazil
In The Last Decade
Jay Li
20 papers receiving 577 citations
Peers
Comparison fields: 5 of 111
- Radiology, Nuclear Medicine and Imaging 181
- Neurology 81
- Cellular and Molecular Neuroscience 65
- Biomedical Engineering 137
- Immunology 64
Countries citing papers authored by Jay Li
This map shows the geographic impact of Jay Li'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 Jay Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jay Li more than expected).
Fields of papers citing papers by Jay Li
This network shows the impact of papers produced by Jay Li. 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 Jay Li. The network helps show where Jay Li may publish in the future.
Co-authors
The 25 scholars most cited alongside Jay Li, 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 | 2006 | 182 | |
| 2 | 2013 | 113 | |
| 3 | 2013 | 84 | |
| 4 | 2019 | 46 | |
| 5 | 2018 | 27 | |
| 6 | 2022 | 23 | |
| 7 | 2020 | 19 | |
| 8 | 2021 | 19 | |
| 9 | 2021 | 18 | |
| 10 | 2020 | 14 | |
| 11 | 2021 | 9 | |
| 12 | 2023 | 8 | |
| 13 | 2019 | 8 | |
| 14 | 2024 | 8 | |
| 15 | 2021 | 2 | |
| 16 | 2001 | 2 | |
| 17 | 2022 | 2 | |
| 18 | System Demonstration of MRAM Co-designed Processing-in-Memory CNN Accelerator for Mobile and IoT Applications. | 2019 | 1 |
| 19 | 2001 | 1 | |
| 20 | 2022 | 1 |
About Jay Li
Jay Li is a scholar working on Neurology, Cellular and Molecular Neuroscience, Biophysics, Hardware and Architecture and Clinical Biochemistry, having authored 20 papers that have together received 587 indexed citations. Recurring topics across this work include Neurological disorders and treatments (5 papers), Genetic Neurodegenerative Diseases (4 papers), Medical Imaging Techniques and Applications (2 papers), Botulinum Toxin and Related Neurological Disorders (2 papers), Advanced X-ray and CT Imaging (2 papers), 3D IC and TSV technologies (2 papers), Advanced MRI Techniques and Applications (2 papers) and Semiconductor Lasers and Optical Devices (2 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (181 citations), Neurology (81 citations), Cellular and Molecular Neuroscience (65 citations), Biomedical Engineering (137 citations) and Immunology (64 citations). Jay Li has collaborated with scholars based in United States, Taiwan and Brazil. Frequent co-authors include Jiang Hsieh, Melissa Vass, Darin Okerlund, Xiangyang Tang, William T. Dauer, Samuel S. Pappas, Irina Grishina, Sumathi Sankaran‐Walters, Thomas Prindiville and Mónica Macal. Their work appears in journals such as eLife, ACS Chemical Neuroscience, Medical Physics, Electroanalysis and Journal of Clinical Investigation.
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.