Jeffrey Li
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 5%
- Explainable Artificial Intelligence (XAI)
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
- Machine Learning and Data Classification
- Machine Learning in Healthcare
- Imbalanced Data Classification Techniques
Papers in
-
- Machine Learning and Data Classification 2
- Explainable Artificial Intelligence (XAI) 2
- Adversarial Robustness in Machine Learning 2
- Co-authors
- Joon Sik Kim (2 shared papers)Ameet Talwalkar (2 shared papers)Valerie Chen (2 shared papers)Gregory Plumb (2 shared papers)Suresh Narayanan (1 shared paper)Qingteng Zhang (1 shared paper)Alec Sandy (1 shared paper)Eric M. Đufresne (1 shared paper)
- Journals
- Journal of Synchrotron Radiation (1 paper)Journal of the American Medical Informatics Association (1 paper)Queue (1 paper)Communications of the ACM (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
Jeffrey Li
5 papers receiving 493 citations
Jeffrey Li's Hit Papers
Peers
Comparison fields: 5 of 133
- Health Informatics 19
- Artificial Intelligence 234
- Health Information Management 14
- Management Science and Operations Research 37
- Safety Research 23
Countries citing papers authored by Jeffrey Li
This map shows the geographic impact of Jeffrey 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 Jeffrey Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeffrey Li more than expected).
Fields of papers citing papers by Jeffrey Li
This network shows the impact of papers produced by Jeffrey 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 Jeffrey Li. The network helps show where Jeffrey Li may publish in the future.
Co-authors
The 23 scholars most cited alongside Jeffrey 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 | Interpretable Machine Learning Hit paper breakdown → | 2021 | 367 |
| 2 | Interpretable machine learning Hit paper breakdown → | 2022 | 138 |
| 3 | 2022 | 9 | |
| 4 | 2023 | 8 | |
| 5 | 2014 | 2 |
About Jeffrey Li
Jeffrey Li is a scholar working on Artificial Intelligence, Political Science and International Relations, Health Information Management, Sociology and Political Science and Health Informatics, having authored 5 papers that have together received 524 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Adversarial Robustness in Machine Learning (2 papers), Hong Kong and Taiwan Politics (1 paper), Advanced X-ray Imaging Techniques (1 paper), Medical Coding and Health Information (1 paper), Freedom of Expression and Defamation (1 paper) and Artificial Intelligence in Healthcare and Education (1 paper). The work is most often cited by research in Health Informatics (19 citations), Artificial Intelligence (234 citations), Health Information Management (14 citations), Management Science and Operations Research (37 citations) and Safety Research (23 citations). Jeffrey Li has collaborated with scholars based in United States and Canada. Frequent co-authors include Joon Sik Kim, Ameet Talwalkar, Valerie Chen, Gregory Plumb, Suresh Narayanan, Qingteng Zhang, Alec Sandy, Eric M. Đufresne, Zhang Jiang and Nicholas Schwarz. Their work appears in journals such as Journal of Synchrotron Radiation, Journal of the American Medical Informatics Association, Queue and Communications 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.