Fereshte Khani

2.4k citations
7 papers · 59 indexed · h-index 5
Topics
Explainable Artificial Intelligence (XAI) (3 papers)Statistical Methods and Inference (2 papers)Adversarial Robustness in Machine Learning (2 papers)
Partner nations
United StatesIran

In The Last Decade

Fereshte Khani

7 papers receiving 57 citations

Peers

Fereshte Khani
Comparison fields: 5 of 37
  • Artificial Intelligence 41
  • Computer Vision and Pattern Recognition 10
  • Information Systems 6
  • Radiology, Nuclear Medicine and Imaging 4
  • Industrial and Manufacturing Engineering 3
Replace Jonathan North Washington with:
Jonathan North Washington United States
Damien Sileo France
Xiang Lisa Li United States
Marco Antonio Sobrevilla Cabezudo Brazil
Agnès Lisowska Switzerland
Zhangyue Yin China
Nicolas Rodolfo Fauceglia United States
George Zerveas United States
Nikola Momchev United States
Hany Hassan Awadalla United States
Fereshte Khani relative to Jonathan North Washington United States Jonathan North Washington's profile →
Citations per field
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Citations per year

Countries citing papers authored by Fereshte Khani

Since Specialization
Citations

This map shows the geographic impact of Fereshte Khani'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 Fereshte Khani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fereshte Khani more than expected).

Fields of papers citing papers by Fereshte Khani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fereshte Khani. 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 Fereshte Khani. The network helps show where Fereshte Khani may publish in the future.

Co-authorship network of co-authors of Fereshte Khani

This figure shows the co-authorship network connecting the top 25 collaborators of Fereshte Khani. A scholar is included among the top collaborators of Fereshte Khani 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 Fereshte Khani. Fereshte Khani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

7 of 7 papers shown
#WorkIndexed citations
1 24
2 4
3 14
4
Noise Induces Loss Discrepancy Across Groups for Linear Regression.
2
5 1
6 10
7 4

About Fereshte Khani

Fereshte Khani is a scholar working on Statistics and Probability, Artificial Intelligence and Media Technology, having authored 7 papers that have together received 59 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (3 papers), Statistical Methods and Inference (2 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Health Informatics (2 citations), Artificial Intelligence (41 citations) and Computer Vision and Pattern Recognition (10 citations). Fereshte Khani has collaborated with scholars based in United States and Iran. Frequent co-authors include Percy Liang, Reid Pryzant, Noah D. Goodman, Hamid Beigy, Zexue He, Marco Túlio Ribeiro and Ahmad Ali Abin. Their work appears in journals such as Transactions of the Association for Computational Linguistics and arXiv (Cornell University).

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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