Ray Li
Impact in
- Biological Psychiatry top 10%
- Tryptophan and brain disorders
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- HER2/EGFR in Cancer Research
Papers in
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- Coding theory and cryptography 7
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- Advanced Data Storage Technologies 5
- Error Correcting Code Techniques 5
- Co-authors
- Venkatesan Guruswami (8 shared papers)Jason H. Williams (2 shared papers)Shilpa Alekar (2 shared papers)Mary Wootters (6 shared papers)Irina Barash (1 shared paper)Vladimı́r Tesař (1 shared paper)Steven Arkin (1 shared paper)Andreas L. Serra (1 shared paper)
- Journals
- IEEE Transactions on Information Theory (4 papers)Clinical Cancer Research (1 paper)Journal of the ACM (1 paper)The Electronic Journal of Combinatorics (1 paper)Investigational New Drugs (1 paper)
- Partner nations
- United StatesPolandSpain
In The Last Decade
Ray Li
24 papers receiving 332 citations
Peers
Comparison fields: 5 of 58
- Biological Psychiatry 30
- Oncology 92
- Immunology 55
- Behavioral Neuroscience 8
- Computational Theory and Mathematics 37
Countries citing papers authored by Ray Li
This map shows the geographic impact of Ray 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 Ray Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ray Li more than expected).
Fields of papers citing papers by Ray Li
This network shows the impact of papers produced by Ray 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 Ray Li. The network helps show where Ray Li may publish in the future.
Co-authors
The 25 scholars most cited alongside Ray 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
Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 69 | |
| 2 | 2017 | 60 | |
| 3 | 2018 | 53 | |
| 4 | 2020 | 48 | |
| 5 | 2016 | 16 | |
| 6 | 2018 | 13 | |
| 7 | 2020 | 11 | |
| 8 | 2024 | 11 | |
| 9 | 2022 | 6 | |
| 10 | 2022 | 6 | |
| 11 | 2022 | 6 | |
| 12 | Lifted Multiplicity Codes. | 2019 | 4 |
| 13 | 2024 | 4 | |
| 14 | 2022 | 4 | |
| 15 | 2020 | 4 | |
| 16 | 2021 | 4 | |
| 17 | 2020 | 3 | |
| 18 | 2021 | 3 | |
| 19 | 2020 | 3 | |
| 20 | 2024 | 3 |
About Ray Li
Ray Li is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computational Theory and Mathematics, Molecular Biology and Discrete Mathematics and Combinatorics, having authored 27 papers that have together received 339 indexed citations. Recurring topics across this work include Coding theory and cryptography (7 papers), DNA and Biological Computing (7 papers), Advanced Graph Theory Research (5 papers), Advanced Data Storage Technologies (5 papers), Error Correcting Code Techniques (5 papers), Advanced biosensing and bioanalysis techniques (5 papers), Limits and Structures in Graph Theory (4 papers) and Complexity and Algorithms in Graphs (3 papers). The work is most often cited by research in Biological Psychiatry (30 citations), Oncology (92 citations), Immunology (55 citations), Behavioral Neuroscience (8 citations) and Computational Theory and Mathematics (37 citations). Ray Li has collaborated with scholars based in United States, Poland and Spain. Frequent co-authors include Venkatesan Guruswami, Jason H. Williams, Shilpa Alekar, Mary Wootters, Irina Barash, Vladimı́r Tesař, Steven Arkin, Andreas L. Serra, Kazimierz Ciechanowski and Matteo Levisetti. Their work appears in journals such as IEEE Transactions on Information Theory, Clinical Cancer Research, Journal of the ACM, The Electronic Journal of Combinatorics and Investigational New Drugs.
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.