Dan Kushnir
Impact in
- Signal Processing top 10%
- Speech and Audio Processing
- Music and Audio Processing
Papers in
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- Machine Learning and Algorithms 3
- Bayesian Methods and Mixture Models 3
- Anomaly Detection Techniques and Applications 3
- Neural Networks and Applications 2
- Machine Learning and Data Classification 2
-
- Network Security and Intrusion Detection 3
- Co-authors
- Achi Brandt (3 shared papers)Ronald R. Coifman (3 shared papers)Meirav Galun (2 shared papers)Veena Mendiratta (2 shared papers)Yair Goldberg (1 shared paper)Ya’acov Ritov (1 shared paper)Alon Zakai (1 shared paper)Hüseyin Uzunalioğlu (3 shared papers)
- Journals
- Applied and Computational Harmonic Analysis (2 papers)Pattern Recognition (1 paper)IEEE Transactions on Signal Processing (1 paper)Physical Review Letters (1 paper)Journal of Machine Learning Research (1 paper)
- Partner nations
- United StatesIsraelGermany
In The Last Decade
Dan Kushnir
14 papers receiving 305 citations
Peers
Comparison fields: 5 of 76
- Computational Mathematics 7
- Signal Processing 63
- Computer Vision and Pattern Recognition 90
- Marketing 32
- Artificial Intelligence 102
Countries citing papers authored by Dan Kushnir
This map shows the geographic impact of Dan Kushnir'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 Dan Kushnir with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Kushnir more than expected).
Fields of papers citing papers by Dan Kushnir
This network shows the impact of papers produced by Dan Kushnir. 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 Dan Kushnir. The network helps show where Dan Kushnir may publish in the future.
Co-authors
The 17 scholars most cited alongside Dan Kushnir, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 50 | |
| 2 | 2011 | 45 | |
| 3 | 2008 | 45 | |
| 4 | 2013 | 45 | |
| 5 | 2011 | 40 | |
| 6 | 2009 | 24 | |
| 7 | 2013 | 16 | |
| 8 | 2006 | 16 | |
| 9 | 2017 | 10 | |
| 10 | 2016 | 9 | |
| 11 | 2014 | 5 | |
| 12 | 2018 | 3 | |
| 13 | Towards Clustering High-dimensional Gaussian Mixture Clouds in Linear Running Time | 2019 | 2 |
| 14 | 2015 | 1 | |
| 15 | 2023 | 0 |
About Dan Kushnir
Dan Kushnir is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Computational Mechanics, having authored 15 papers that have together received 311 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (3 papers), Bayesian Methods and Mixture Models (3 papers), Network Security and Intrusion Detection (3 papers), Anomaly Detection Techniques and Applications (3 papers), Face and Expression Recognition (2 papers), Neural Networks and Applications (2 papers), Topological and Geometric Data Analysis (2 papers) and Machine Learning and Data Classification (2 papers). The work is most often cited by research in Computational Mathematics (7 citations), Signal Processing (63 citations), Computer Vision and Pattern Recognition (90 citations), Marketing (32 citations) and Artificial Intelligence (102 citations). Dan Kushnir has collaborated with scholars based in United States, Israel and Germany. Frequent co-authors include Achi Brandt, Ronald R. Coifman, Meirav Galun, Veena Mendiratta, Yair Goldberg, Ya’acov Ritov, Alon Zakai, Hüseyin Uzunalioğlu, Derek Doran and Israel Cohen. Their work appears in journals such as Applied and Computational Harmonic Analysis, Pattern Recognition, IEEE Transactions on Signal Processing, Physical Review Letters and Journal of Machine Learning Research.
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.