Sayna Ebrahimi

944 total citations · 1 hit paper
10 papers, 241 citations indexed

About

Sayna Ebrahimi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Mechanics of Materials. According to data from OpenAlex, Sayna Ebrahimi has authored 10 papers receiving a total of 241 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Mechanics of Materials. Recurrent topics in Sayna Ebrahimi's work include Domain Adaptation and Few-Shot Learning (6 papers), Multimodal Machine Learning Applications (3 papers) and Topic Modeling (2 papers). Sayna Ebrahimi is often cited by papers focused on Domain Adaptation and Few-Shot Learning (6 papers), Multimodal Machine Learning Applications (3 papers) and Topic Modeling (2 papers). Sayna Ebrahimi collaborates with scholars based in United States, Germany and Argentina. Sayna Ebrahimi's 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 and has published in prestigious journals such as ACM Computing Surveys, Engineering Analysis with Boundary Elements and Journal of mechanics of materials and structures.

In The Last Decade

Sayna Ebrahimi

9 papers receiving 231 citations

Hit Papers

Contrastive Test-Time Adaptation 2022 2026 2023 2024 2022 40 80 120
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Contrastive Test-Time Adaptation
    2022 136 citations Dian Chen, Dequan Wang et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →

Peers

Sayna Ebrahimi
Limin Su China
Enrico Fini Italy
Yiru Wang China
Waldir R. De Almeida Brazil
Linjun Zhou China
Dapeng Hu China
Alok Jain India
Harold Brayan Arteaga-Arteaga Colombia
Zunaira Rauf Pakistan
Shell Xu Hu United Kingdom
Limin Su China
Sayna Ebrahimi
Citations per year, relative to Sayna Ebrahimi Sayna Ebrahimi (= 1×) peers Limin Su

Countries citing papers authored by Sayna Ebrahimi

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

10 of 10 papers shown
1.
Xu, Zihao, et al.. (2025). Continual Learning of Large Language Models: A Comprehensive Survey. ACM Computing Surveys. 58(5). 1–42. 8 indexed citations
2.
Chen, Jiefeng, Jinsung Yoon, Sayna Ebrahimi, et al.. (2023). Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs. 5190–5213. 8 indexed citations
3.
Reed, Colorado, Xiangyu Yue, Sayna Ebrahimi, et al.. (2022). Self-Supervised Pretraining Improves Self-Supervised Pretraining. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 1050–1060. 45 indexed citations
4.
Chen, Dian, Dequan Wang, Trevor Darrell, & Sayna Ebrahimi. (2022). Contrastive Test-Time Adaptation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 295–305. 136 indexed citations breakdown →
5.
Ebrahimi, Sayna, Mohamed Elhoseiny, Trevor Darrell, & Marcus Rohrbach. (2019). Uncertainty-Guided Continual Learning in Bayesian Neural Networks - Extended Abstract.. Computer Vision and Pattern Recognition. 75–78. 2 indexed citations
6.
Ebrahimi, Sayna, et al.. (2019). Generalized Zero-Shot Learning via Aligned Variational Autoencoders. Computer Vision and Pattern Recognition. 54–57. 4 indexed citations
7.
Ebrahimi, Sayna, et al.. (2019). Cross-Linked Variational Autoencoders for Generalized Zero-Shot Learning. 1 indexed citations
8.
Ebrahimi, Sayna, Anna Rohrbach, & Trevor Darrell. (2017). Gradient-free Policy Architecture Search and Adaptation. MPG.PuRe (Max Planck Society). 505–514. 7 indexed citations
9.
Ebrahimi, Sayna, David J. Steigmann, & K. Komvopoulos. (2015). Peridynamics analysis of the nanoscale friction and wear properties of amorphous carbon thin films. Journal of mechanics of materials and structures. 10(5). 559–572. 24 indexed citations
10.
Ebrahimi, Sayna & A.‐V. Phan. (2013). Dynamic analysis of cracks using the SGBEM for elastodynamics in the Laplace-space frequency domain. Engineering Analysis with Boundary Elements. 37(11). 1378–1391. 6 indexed citations

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.

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