O. Amram
Impact in
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- Particle physics theoretical and experimental studies
- Particle Detector Development and Performance
- High-Energy Particle Collisions Research
- Neutrino Physics Research
- Quantum Chromodynamics and Particle Interactions
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- Computational Physics and Python Applications
- Gaussian Processes and Bayesian Inference
- Anomaly Detection Techniques and Applications
Papers in
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- Particle physics theoretical and experimental studies 3
- Particle Detector Development and Performance 1
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- Computational Physics and Python Applications 2
- Gaussian Processes and Bayesian Inference 1
- Co-authors
- C. Mantilla (1 shared paper)K. Pedro (1 shared paper)Kirsten Hall (1 shared paper)Allison Kirkpatrick (1 shared paper)Kirill Tchernyshyov (1 shared paper)Arianna S. Long (1 shared paper)Duncan J. Watts (1 shared paper)Timothy M. Heckman (1 shared paper)
- Journals
- Journal of High Energy Physics (2 papers)Physical review. D (1 paper)Machine Learning Science and Technology (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesNorwayItaly
In The Last Decade
O. Amram
5 papers receiving 87 citations
Peers
Comparison fields: 5 of 21
- Nuclear and High Energy Physics 70
- Artificial Intelligence 39
- Instrumentation 3
- Radiation 4
- Astronomy and Astrophysics 7
Countries citing papers authored by O. Amram
This map shows the geographic impact of O. Amram'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 O. Amram with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites O. Amram more than expected).
Fields of papers citing papers by O. Amram
This network shows the impact of papers produced by O. Amram. 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 O. Amram. The network helps show where O. Amram may publish in the future.
Co-authors
The 15 scholars most cited alongside O. Amram, 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 | 2021 | 49 | |
| 2 | 2023 | 27 | |
| 3 | 2021 | 7 | |
| 4 | 2025 | 3 | |
| 5 | 2025 | 1 |
About O. Amram
O. Amram is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence, Astronomy and Astrophysics, Computer Vision and Pattern Recognition and Ecology, having authored 5 papers that have together received 87 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (3 papers), Computational Physics and Python Applications (2 papers), Galaxies: Formation, Evolution, Phenomena (1 paper), Remote Sensing in Agriculture (1 paper), Superconducting Materials and Applications (1 paper), Advanced Vision and Imaging (1 paper), Particle Detector Development and Performance (1 paper) and Gaussian Processes and Bayesian Inference (1 paper). The work is most often cited by research in Nuclear and High Energy Physics (70 citations), Artificial Intelligence (39 citations), Instrumentation (3 citations), Radiation (4 citations) and Astronomy and Astrophysics (7 citations). O. Amram has collaborated with scholars based in United States, Norway and Italy. Frequent co-authors include C. Mantilla, K. Pedro, Kirsten Hall, Allison Kirkpatrick, Kirill Tchernyshyov, Arianna S. Long, Duncan J. Watts, Timothy M. Heckman, Eileen T. Meyer and M. Chiaberge. Their work appears in journals such as Journal of High Energy Physics, Physical review. D, Machine Learning Science and Technology 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.