Sayna Ebrahimi
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Mechanics of Materials
- Radiology, Nuclear Medicine and Imaging
- Civil and Structural Engineering
- Co-authors
- Trevor DarrellDequan WangDian ChenK. KomvopoulosDavid J. SteigmannSean L. MetzgerBo LiShanghang Zhang
- Topics
- Domain Adaptation and Few-Shot Learning (6 papers)Multimodal Machine Learning Applications (3 papers)Topic Modeling (2 papers)
- Journals
- ACM Computing SurveysEngineering Analysis with Boundary ElementsJournal of mechanics of materials and structures
- Partner nations
- United StatesGermanyArgentina
In The Last Decade
Sayna Ebrahimi
9 papers receiving 231 citations
Hit Papers
Peers
Comparison fields: 5 of 69
- Artificial Intelligence 144
- Computer Vision and Pattern Recognition 109
- Mechanics of Materials 32
- Radiology, Nuclear Medicine and Imaging 21
- Civil and Structural Engineering 20
Countries citing papers authored by Sayna Ebrahimi
This map shows the geographic impact of Sayna Ebrahimi'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 Sayna Ebrahimi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sayna Ebrahimi more than expected).
Fields of papers citing papers by Sayna Ebrahimi
This network shows the impact of papers produced by Sayna Ebrahimi. 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 Sayna Ebrahimi. The network helps show where Sayna Ebrahimi may publish in the future.
Co-authorship network of co-authors of Sayna Ebrahimi
This figure shows the co-authorship network connecting the top 25 collaborators of Sayna Ebrahimi. A scholar is included among the top collaborators of Sayna Ebrahimi 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 Sayna Ebrahimi. Sayna Ebrahimi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 8 | |
| 3 | 45 | |
| 4 | Contrastive Test-Time Adaptationbreakdown → | 136 |
| 5 | Uncertainty-Guided Continual Learning in Bayesian Neural Networks - Extended Abstract. | 2 |
| 6 | Generalized Zero-Shot Learning via Aligned Variational Autoencoders | 4 |
| 7 | Cross-Linked Variational Autoencoders for Generalized Zero-Shot Learning | 1 |
| 8 | Gradient-free Policy Architecture Search and Adaptation | 7 |
| 9 | 24 | |
| 10 | 6 |
About Sayna Ebrahimi
Sayna Ebrahimi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, having authored 10 papers that have together received 241 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (6 papers), Multimodal Machine Learning Applications (3 papers) and Topic Modeling (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (109 citations), Artificial Intelligence (144 citations) and Media Technology (15 citations). Sayna Ebrahimi has collaborated with scholars based in United States, Germany and Argentina. Frequent co-authors include Trevor Darrell, Dequan Wang, Dian Chen, K. Komvopoulos, David J. Steigmann, Sean L. Metzger, Bo Li, Shanghang Zhang, Colorado Reed and Xiangyu Yue. Their work appears in journals such as ACM Computing Surveys, Engineering Analysis with Boundary Elements and Journal of mechanics of materials and structures.
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.