Amir Saffari

7.6k total citations · 3 hit papers
56 papers, 3.9k citations indexed

About

Amir Saffari is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Ocean Engineering. According to data from OpenAlex, Amir Saffari has authored 56 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 16 papers in Computer Vision and Pattern Recognition and 12 papers in Ocean Engineering. Recurrent topics in Amir Saffari's work include Coal Properties and Utilization (12 papers), Video Surveillance and Tracking Methods (9 papers) and Topic Modeling (8 papers). Amir Saffari is often cited by papers focused on Coal Properties and Utilization (12 papers), Video Surveillance and Tracking Methods (9 papers) and Topic Modeling (8 papers). Amir Saffari collaborates with scholars based in Iran, Austria and Germany. Amir Saffari's co-authors include Philip H. S. Torr, Sam Hare, Horst Bischof, Christian Leistner, Stuart Golodetz, Ming‐Ming Cheng, Stephen L. Hicks, Vibhav Vineet, Martin Godec and Thomas Pock and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Journal of Fluid Mechanics.

In The Last Decade

Amir Saffari

53 papers receiving 3.8k citations

Hit Papers

Struck: Structured output tracking with kernels 2011 2026 2016 2021 2011 2015 2020 400 800 1.2k
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. Struck: Structured output tracking with kernels
    2011 1.3k citations Amir Saffari et al. profile →
  2. Struck: Structured Output Tracking with Kernels
    2015 719 citations Amir Saffari et al. profile →
  3. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
    2020 350 citations Monika A. Myszczynska, Alix M.B. Lacoste et al. Nature Reviews Neurology profile →

Peers

Amir Saffari
Xin Zhao China
Matej Kristan Slovenia
Zhenyu He China
Honggang Qi China
Lijun Wang China
Moulay A. Akhloufi Canada
Jianke Zhu China
Xiangyuan Lan China
Bohyung Han South Korea
Houwen Peng China
Xin Zhao China
Amir Saffari
Citations per year, relative to Amir Saffari Amir Saffari (= 1×) peers Xin Zhao

Countries citing papers authored by Amir Saffari

Since Specialization
Citations

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

Fields of papers citing papers by Amir Saffari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amir Saffari

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

All Works

20 of 20 papers shown
1.
Baek, Jinheon, et al.. (2023). Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering. 70–98. 23 indexed citations
2.
Baek, Jinheon, Alham Fikri Aji, & Amir Saffari. (2023). Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering. 78–106. 29 indexed citations
3.
Mavadia, Sandeep, et al.. (2023). Knowledge Graph-augmented Language Models for Complex Question Answering. 1–8. 11 indexed citations
4.
Cheng, Weiwei, et al.. (2023). mReFinED: An Efficient End-to-End Multilingual Entity Linking System. 15080–15089. 1 indexed citations
5.
Saffari, Amir, Farhang Sereshki, & Mohammad Ataei. (2020). A comprehensive study on the effect of moisture content on coal spontaneous combustion tendency. 12(3). 194–204. 1 indexed citations
6.
Myszczynska, Monika A., Alix M.B. Lacoste, Daniel Neil, et al.. (2020). Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nature Reviews Neurology. 16(8). 440–456. 350 indexed citations breakdown →
7.
Saffari, Amir, Mohammad Ataei, & Farhang Sereshki. (2019). Studying relationship between coal intrinsic characteristics in spontaneous combustion of coal potential using crossing point temperature test method A case study: Tabas Parvadeh coal mines, Iran. Journal of mining and environment. 11(1). 315–333. 1 indexed citations
8.
Ataei, Mohammad, et al.. (2018). Determination and assessment of coal bed methane potential using rock engineering systems. Journal of mining and environment. 9(3). 605–621. 7 indexed citations
9.
10.
Sereshki, Farhang, et al.. (2016). Effects of Ratcheting Strain on Cyclic Time-Dependent Parameters of Salt Rocks. 2(1). 1 indexed citations
11.
Sereshki, Farhang, et al.. (2016). Evaluation of the effect of macerals on coal permeability in Tazareh and Parvadeh mines. SHILAP Revista de lepidopterología. 4 indexed citations
12.
Saffari, Amir, et al.. (2016). Determining an appropriate method for the purpose of land allocation for ecotourism development (case study: Taleghan County, Iran). Environmental Monitoring and Assessment. 188(12). 651–651. 11 indexed citations
13.
Saffari, Amir, et al.. (2013). Applying Rock Engineering Systems (RES) approach to Evaluate and Classify the Coal Spontaneous Combustion Potential in Eastern Alborz Coal Mines. SHILAP Revista de lepidopterología. 22 indexed citations
14.
Schulter, Samuel, Paul Wohlhart, Christian Leistner, et al.. (2013). Alternating Decision Forests. 508–515. 42 indexed citations
15.
Zhang, Ziming, Lubor Ladicky, Philip H. S. Torr, & Amir Saffari. (2011). Learning Anchor Planes for Classification. Oxford University Research Archive (ORA) (University of Oxford). 24. 1611–1619. 17 indexed citations
16.
Guyon, Isabelle, Amir Saffari, Gideon Dror, & Gavin C. Cawley. (2010). Model Selection: Beyond the Bayesian/Frequentist Divide. Journal of Machine Learning Research. 11(3). 61–87. 93 indexed citations
17.
Saffari, Amir, et al.. (2009). On-line Random Forests. 1393–1400. 335 indexed citations
18.
Unger, Markus, et al.. (2009). Interactive Texture Segmentation using Random Forests and Total Variation. 66.1–66.12. 21 indexed citations
19.
Guyon, Isabelle, Amir Saffari, Gideon Dror, & Gavin C. Cawley. (2007). Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge. Neural Networks. 21(2-3). 544–550. 16 indexed citations
20.
Saffari, Amir. (2006). Book Review: "COMPLEX WORLDS FROM SIMPLER NERVOUS SYSTEMS", BY FREDERICK R. PRETE (Editor). International Journal of Computational Intelligence and Applications. 6(4). 569–572.

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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