Danica J. Sutherland
- Astronomy and Astrophysics top 10%
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Instrumentation
- Statistics and Probability
- Co-authors
- Jeff SchneiderBarnabás PóczosHy TracMichelle NtampakaNicholas BattagliaLiang XiongS. FromenteauMikołaj Bińkowski
- Topics
- Statistical Methods and Inference (4 papers)Generative Adversarial Networks and Image Synthesis (2 papers)Machine Learning and Algorithms (2 papers)
- Journals
- The Astrophysical JournalJournal of Artificial Intelligence ResearchJournal of Interprofessional Care
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Danica J. Sutherland
15 papers receiving 163 citations
Peers
Comparison fields: 5 of 49
- Astronomy and Astrophysics 73
- Artificial Intelligence 63
- Computer Vision and Pattern Recognition 36
- Instrumentation 23
- Statistics and Probability 19
Countries citing papers authored by Danica J. Sutherland
This map shows the geographic impact of Danica J. Sutherland'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 Danica J. Sutherland with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danica J. Sutherland more than expected).
Fields of papers citing papers by Danica J. Sutherland
This network shows the impact of papers produced by Danica J. Sutherland. 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 Danica J. Sutherland. The network helps show where Danica J. Sutherland may publish in the future.
Co-authorship network of co-authors of Danica J. Sutherland
This figure shows the co-authorship network connecting the top 25 collaborators of Danica J. Sutherland. A scholar is included among the top collaborators of Danica J. Sutherland based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Danica J. Sutherland. Danica J. Sutherland is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 0 | |
| 7 | 5 | |
| 8 | On gradient regularizers for MMD GANs | 7 |
| 9 | 2 | |
| 10 | Efficient and principled score estimation. | 1 |
| 11 | 5 | |
| 12 | 32 | |
| 13 | Scalable, Flexible and Active Learning on Distributions | 3 |
| 14 | 51 | |
| 15 | Active Pointillistic Pattern Search | 2 |
| 16 | 14 | |
| 17 | 22 | |
| 18 | 20 |
About Danica J. Sutherland
Danica J. Sutherland is a scholar working on Instrumentation, Statistics and Probability and Artificial Intelligence, having authored 18 papers that have together received 170 indexed citations. Recurring topics across this work include Statistical Methods and Inference (4 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Instrumentation (23 citations), Astronomy and Astrophysics (73 citations) and Statistics and Probability (19 citations). Danica J. Sutherland has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Jeff Schneider, Barnabás Póczos, Hy Trac, Michelle Ntampaka, Nicholas Battaglia, Liang Xiong, S. Fromenteau, Mikołaj Bińkowski, Michael Arbel and Arthur Gretton. Their work appears in journals such as The Astrophysical Journal, Journal of Artificial Intelligence Research and Journal of Interprofessional Care.
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.