Cliff Brunk
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Text and Document Classification Technologies
- Speech Recognition and Synthesis
- Signal Processing top 10%
- Data Management and Algorithms
Papers in
-
- Text and Document Classification Technologies 2
- Machine Learning and Data Classification 2
- Topic Modeling 2
- Natural Language Processing Techniques 1
-
- Web Data Mining and Analysis 2
- Co-authors
- Oriol Vinyals (1 shared paper)Dan Gillick (1 shared paper)Amarnag Subramanya (1 shared paper)James D. Kelly (1 shared paper)Ron Kohavi (1 shared paper)Dmitry Pavlov (1 shared paper)C. Lee Giles (2 shared papers)Ziming Zhuang (2 shared papers)
- Journals
- Proceedings of the American Society for Information Science and Technology (1 paper)Knowledge Discovery and Data Mining (1 paper)
- Partner nations
- United StatesRussiaUnited Kingdom
In The Last Decade
Cliff Brunk
7 papers receiving 188 citations
Peers
Comparison fields: 5 of 38
- Artificial Intelligence 154
- Signal Processing 46
- Computer Vision and Pattern Recognition 56
- Information Systems 54
- Geography, Planning and Development 7
Countries citing papers authored by Cliff Brunk
This map shows the geographic impact of Cliff Brunk'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 Cliff Brunk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cliff Brunk more than expected).
Fields of papers citing papers by Cliff Brunk
This network shows the impact of papers produced by Cliff Brunk. 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 Cliff Brunk. The network helps show where Cliff Brunk may publish in the future.
Co-authors
The 12 scholars most cited alongside Cliff Brunk, 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 | 2016 | 112 | |
| 2 | 1997 | 58 | |
| 3 | 2010 | 19 | |
| 4 | 2008 | 12 | |
| 5 | 2012 | 5 | |
| 6 | 2002 | 3 | |
| 7 | 2008 | 1 |
About Cliff Brunk
Cliff Brunk is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Signal Processing and Geography, Planning and Development, having authored 7 papers that have together received 210 indexed citations. Recurring topics across this work include Data Management and Algorithms (2 papers), Text and Document Classification Technologies (2 papers), Machine Learning and Data Classification (2 papers), Topic Modeling (2 papers), Web Data Mining and Analysis (2 papers), Geographic Information Systems Studies (2 papers), Natural Language Processing Techniques (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Artificial Intelligence (154 citations), Signal Processing (46 citations), Computer Vision and Pattern Recognition (56 citations), Information Systems (54 citations) and Geography, Planning and Development (7 citations). Cliff Brunk has collaborated with scholars based in United States, Russia and United Kingdom. Frequent co-authors include Oriol Vinyals, Dan Gillick, Amarnag Subramanya, James D. Kelly, Ron Kohavi, Dmitry Pavlov, C. Lee Giles, Ziming Zhuang, Prasenjit Mitra and Sumit Bhatia. Their work appears in journals such as Proceedings of the American Society for Information Science and Technology and Knowledge Discovery and Data Mining.
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.