Daisy Yi Ding
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Machine Learning in Healthcare
- Explainable Artificial Intelligence (XAI)
Papers in
-
- Topic Modeling 2
- Machine Learning in Healthcare 2
- Explainable Artificial Intelligence (XAI) 2
-
- Biomedical Text Mining and Ontologies 2
- Single-cell and spatial transcriptomics 1
- Co-authors
- Yuhao Zhang (1 shared paper)Christopher D. Manning (1 shared paper)Curtis P. Langlotz (1 shared paper)Robert Tibshirani (3 shared papers)Balasubramanian Narasimhan (1 shared paper)Tony Duan (1 shared paper)Andrew Y. Ng (1 shared paper)Anand Avati (1 shared paper)
- Journals
- npj Digital Medicine (1 paper)Proceedings of the National Academy of Sciences (1 paper)Nature Genetics (1 paper)PubMed (1 paper)bioRxiv (Cold Spring Harbor Laboratory) (2 papers)
- Partner nations
- United StatesUnited KingdomSweden
In The Last Decade
Daisy Yi Ding
7 papers receiving 283 citations
Peers
Comparison fields: 5 of 83
- Health Informatics 35
- Artificial Intelligence 136
- Computer Science Applications 8
- Health Information Management 6
- Statistics, Probability and Uncertainty 9
Countries citing papers authored by Daisy Yi Ding
This map shows the geographic impact of Daisy Yi Ding'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 Daisy Yi Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daisy Yi Ding more than expected).
Fields of papers citing papers by Daisy Yi Ding
This network shows the impact of papers produced by Daisy Yi Ding. 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 Daisy Yi Ding. The network helps show where Daisy Yi Ding may publish in the future.
Co-authors
The 25 scholars most cited alongside Daisy Yi Ding, 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 | 85 | |
| 2 | 2018 | 84 | |
| 3 | NGBoost: Natural Gradient Boosting for Probabilistic Prediction | 2020 | 71 |
| 4 | 2022 | 40 | |
| 5 | 2018 | 13 | |
| 6 | 2025 | 3 | |
| 7 | 2024 | 2 | |
| 8 | 2026 | 0 | |
| 9 | 2026 | 0 |
About Daisy Yi Ding
Daisy Yi Ding is a scholar working on Artificial Intelligence, Molecular Biology, Infectious Diseases, Health Information Management and Computer Vision and Pattern Recognition, having authored 9 papers that have together received 298 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Machine Learning in Healthcare (2 papers), Biomedical Text Mining and Ontologies (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Advanced Vision and Imaging (1 paper), Advanced Data Compression Techniques (1 paper) and Single-cell and spatial transcriptomics (1 paper). The work is most often cited by research in Health Informatics (35 citations), Artificial Intelligence (136 citations), Computer Science Applications (8 citations), Health Information Management (6 citations) and Statistics, Probability and Uncertainty (9 citations). Daisy Yi Ding has collaborated with scholars based in United States, United Kingdom and Sweden. Frequent co-authors include Yuhao Zhang, Christopher D. Manning, Curtis P. Langlotz, Robert Tibshirani, Balasubramanian Narasimhan, Tony Duan, Andrew Y. Ng, Anand Avati, Khanh K. Thai and Alejandro Schuler. Their work appears in journals such as npj Digital Medicine, Proceedings of the National Academy of Sciences, Nature Genetics, PubMed and bioRxiv (Cold Spring Harbor Laboratory).
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.