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 8
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- Advanced Data Storage Technologies 6
- Error Correcting Code Techniques 5
- Co-authors
- Venkatesan Guruswami (10 shared papers)Jason H. Williams (2 shared papers)Shilpa Alekar (2 shared papers)Mary Wootters (6 shared papers)Matteo Levisetti (1 shared paper)Steven Arkin (1 shared paper)Andreas L. Serra (1 shared paper)Kazimierz Ciechanowski (1 shared paper)
- Journals
- IEEE Transactions on Information Theory (4 papers)Journal of the American Society of Nephrology (1 paper)Clinical Cancer Research (1 paper)Quantum Science and Technology (1 paper)Journal of the ACM (1 paper)
- Partner nations
- United StatesPolandSpain
In The Last Decade
Ray Li
27 papers receiving 350 citations
Peers
Comparison fields: 5 of 58
- Biological Psychiatry 31
- Oncology 92
- Immunology 56
- Computer Networks and Communications 59
- Behavioral Neuroscience 8
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 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 69 | |
| 2 | 2017 | 61 | |
| 3 | 2018 | 54 | |
| 4 | 2020 | 49 | |
| 5 | 2016 | 16 | |
| 6 | 2018 | 14 | |
| 7 | 2020 | 11 | |
| 8 | 2024 | 11 | |
| 9 | 2022 | 7 | |
| 10 | 2018 | 6 | |
| 11 | 2022 | 6 | |
| 12 | 2022 | 6 | |
| 13 | 2024 | 5 | |
| 14 | Lifted Multiplicity Codes. | 2019 | 4 |
| 15 | 2024 | 4 | |
| 16 | 2022 | 4 | |
| 17 | 2020 | 4 | |
| 18 | 2020 | 4 | |
| 19 | 2021 | 4 | |
| 20 | 2020 | 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 30 papers that have together received 358 indexed citations. Recurring topics across this work include Coding theory and cryptography (8 papers), DNA and Biological Computing (8 papers), Advanced Data Storage Technologies (6 papers), Advanced biosensing and bioanalysis techniques (6 papers), Advanced Graph Theory Research (5 papers), Error Correcting Code 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 (31 citations), Oncology (92 citations), Immunology (56 citations), Computer Networks and Communications (59 citations) and Behavioral Neuroscience (8 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, Matteo Levisetti, Steven Arkin, Andreas L. Serra, Kazimierz Ciechanowski, Irina Barash and York Pei. Their work appears in journals such as IEEE Transactions on Information Theory, Journal of the American Society of Nephrology, Clinical Cancer Research, Quantum Science and Technology and Journal of the ACM.
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.