Dylan Rankin
Impact in
- Nuclear and High Energy Physics top 10%
- Particle physics theoretical and experimental studies
- Particle Detector Development and Performance
- Nuclear physics research studies
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- Parallel Computing and Optimization Techniques
Papers in
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- Seismology and Earthquake Studies 2
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- Pulsars and Gravitational Waves Research 5
- Gamma-ray bursts and supernovae 5
- Co-authors
- Philip Harris (11 shared papers)Vladimir Lončar (7 shared papers)Giuseppe Di Guglielmo (4 shared papers)Z. Wu (5 shared papers)J. Ngadiuba (3 shared papers)Duc Hoang (2 shared papers)M. Pierini (3 shared papers)J. Duarte (4 shared papers)
- Journals
- Machine Learning Science and Technology (2 papers)Physical review. D (1 paper)Nature Astronomy (1 paper)Classical and Quantum Gravity (1 paper)Journal of Instrumentation (1 paper)
- Partner nations
- United StatesSwitzerlandUnited Kingdom
In The Last Decade
Dylan Rankin
16 papers receiving 187 citations
Peers
Comparison fields: 5 of 39
- Nuclear and High Energy Physics 81
- Hardware and Architecture 23
- Artificial Intelligence 76
- Computer Vision and Pattern Recognition 39
- Radiation 16
Countries citing papers authored by Dylan Rankin
This map shows the geographic impact of Dylan Rankin'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 Dylan Rankin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dylan Rankin more than expected).
Fields of papers citing papers by Dylan Rankin
This network shows the impact of papers produced by Dylan Rankin. 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 Dylan Rankin. The network helps show where Dylan Rankin may publish in the future.
Co-authors
The 25 scholars most cited alongside Dylan Rankin, 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 | 2020 | 50 | |
| 2 | Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML | 2020 | 44 |
| 3 | 2021 | 25 | |
| 4 | 1954 | 12 | |
| 5 | 1952 | 12 | |
| 6 | 2022 | 12 | |
| 7 | 2024 | 8 | |
| 8 | 2023 | 8 | |
| 9 | 2021 | 7 | |
| 10 | 2025 | 5 | |
| 11 | 2019 | 5 | |
| 12 | 2020 | 4 | |
| 13 | 2022 | 3 | |
| 14 | 2024 | 2 | |
| 15 | 2025 | 2 | |
| 16 | 2025 | 1 |
About Dylan Rankin
Dylan Rankin is a scholar working on Artificial Intelligence, Astronomy and Astrophysics, Nuclear and High Energy Physics, Atomic and Molecular Physics, and Optics and Hardware and Architecture, having authored 16 papers that have together received 200 indexed citations. Recurring topics across this work include Pulsars and Gravitational Waves Research (5 papers), Gamma-ray bursts and supernovae (5 papers), Particle Detector Development and Performance (4 papers), Particle physics theoretical and experimental studies (4 papers), Atomic and Subatomic Physics Research (3 papers), Parallel Computing and Optimization Techniques (3 papers), Seismology and Earthquake Studies (2 papers) and Quantum, superfluid, helium dynamics (2 papers). The work is most often cited by research in Nuclear and High Energy Physics (81 citations), Hardware and Architecture (23 citations), Artificial Intelligence (76 citations), Computer Vision and Pattern Recognition (39 citations) and Radiation (16 citations). Dylan Rankin has collaborated with scholars based in United States, Switzerland and United Kingdom. Frequent co-authors include Philip Harris, Vladimir Lončar, Giuseppe Di Guglielmo, Z. Wu, J. Ngadiuba, Duc Hoang, M. Pierini, J. Duarte, Silviu‐Marian Udrescu and T. F. Stratton. Their work appears in journals such as Machine Learning Science and Technology, Physical review. D, Nature Astronomy, Classical and Quantum Gravity and Journal of Instrumentation.
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.