George Zerveas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Information Systems
- Sociology and Political Science
- Signal Processing
- Co-authors
- Carsten EickhoffNavid RekabsazDaniel J. CohenDaniel A. CohenFábio CrestaniMarkus SchedlDaniel CohenWilliam J. Rudman
- Topics
- Topic Modeling (4 papers)Natural Language Processing Techniques (4 papers)Domain Adaptation and Few-Shot Learning (2 papers)
- Journals
- ACM Transactions on Information SystemsarXiv (Cornell University)Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Partner nations
- United StatesAustriaGermany
In The Last Decade
George Zerveas
7 papers receiving 32 citations
Peers
Comparison fields: 5 of 14
- Artificial Intelligence 28
- Computer Vision and Pattern Recognition 7
- Information Systems 5
- Sociology and Political Science 3
- Signal Processing 3
Countries citing papers authored by George Zerveas
This map shows the geographic impact of George Zerveas'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 George Zerveas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites George Zerveas more than expected).
Fields of papers citing papers by George Zerveas
This network shows the impact of papers produced by George Zerveas. 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 George Zerveas. The network helps show where George Zerveas may publish in the future.
Co-authorship network of co-authors of George Zerveas
This figure shows the co-authorship network connecting the top 25 collaborators of George Zerveas. A scholar is included among the top collaborators of George Zerveas 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 George Zerveas. George Zerveas 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 | 0 | |
| 3 | 4 | |
| 4 | 6 | |
| 5 | 10 | |
| 6 | Representation Learning of Multivariate Time Series using a Transformer Framework | 2 |
| 7 | 8 | |
| 8 | 1 |
About George Zerveas
George Zerveas is a scholar working on Artificial Intelligence, Emergency Medicine and Signal Processing, having authored 8 papers that have together received 32 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Natural Language Processing Techniques (4 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Artificial Intelligence (28 citations), Computer Vision and Pattern Recognition (7 citations) and Communication (2 citations). George Zerveas has collaborated with scholars based in United States, Austria and Germany. Frequent co-authors include Carsten Eickhoff, Navid Rekabsaz, Daniel J. Cohen, Daniel A. Cohen, Fábio Crestani, Markus Schedl, Daniel Cohen, William J. Rudman, Dhaval Patel and Anuradha Bhamidipaty. Their work appears in journals such as ACM Transactions on Information Systems, arXiv (Cornell University) and Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.
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.