K. Pedro
Impact in
- Nuclear and High Energy Physics top 10%
- Particle physics theoretical and experimental studies
- Particle Detector Development and Performance
- High-Energy Particle Collisions Research
- Dark Matter and Cosmic Phenomena
-
- Radiation Detection and Scintillator Technologies
Papers in ⓘ
-
- Particle physics theoretical and experimental studies 12
- Particle Detector Development and Performance 11
- Dark Matter and Cosmic Phenomena 2
-
- Radiation Detection and Scintillator Technologies 4
- Co-authors
- O. Amram (1 shared paper)M. Pierini (2 shared papers)L. T. Le Pottier (1 shared paper)F. Canelli (1 shared paper)A. De Cosa (1 shared paper)J. Niedziela (1 shared paper)S. Jindariani (1 shared paper)J. Ngadiuba (1 shared paper)
- Journals
- Machine Learning Science and Technology (2 papers)Journal of High Energy Physics (1 paper)Physical review. D (1 paper)SciPost Physics Core (1 paper)Frontiers in Physics (1 paper)
- Partner nations
- United StatesSwitzerlandThailand
In The Last Decade
K. Pedro
15 papers receiving 142 citations
Peers
Comparison fields: 5 of 36
- Nuclear and High Energy Physics 83
- Radiation 15
- Computer Vision and Pattern Recognition 34
- Artificial Intelligence 49
- Hardware and Architecture 8
Countries citing papers authored by K. Pedro
This map shows the geographic impact of K. Pedro'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 K. Pedro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites K. Pedro more than expected).
Fields of papers citing papers by K. Pedro
This network shows the impact of papers produced by K. Pedro. 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 K. Pedro. The network helps show where K. Pedro may publish in the future.
Co-authors
The 25 scholars most cited alongside K. Pedro, 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 | Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML | 2020 | 44 |
| 2 | 2023 | 24 | |
| 3 | 2022 | 23 | |
| 4 | 2022 | 14 | |
| 5 | 2023 | 8 | |
| 6 | 2022 | 6 | |
| 7 | 2019 | 6 | |
| 8 | 2024 | 4 | |
| 9 | 2019 | 4 | |
| 10 | 2023 | 3 | |
| 11 | 2023 | 3 | |
| 12 | 2021 | 3 | |
| 13 | 2023 | 2 | |
| 14 | 2023 | 1 | |
| 15 | 2024 | 1 | |
| 16 | 2024 | 0 | |
| 17 | 2018 | 0 |
About K. Pedro
K. Pedro is a scholar working on Nuclear and High Energy Physics, Radiation, Astronomy and Astrophysics, Statistics and Probability and Artificial Intelligence, having authored 17 papers that have together received 146 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (12 papers), Particle Detector Development and Performance (11 papers), Radiation Detection and Scintillator Technologies (4 papers), Dark Matter and Cosmic Phenomena (2 papers), Distributed and Parallel Computing Systems (2 papers), Superconducting Materials and Applications (2 papers), Advanced Statistical Methods and Models (2 papers) and Cosmology and Gravitation Theories (2 papers). The work is most often cited by research in Nuclear and High Energy Physics (83 citations), Radiation (15 citations), Computer Vision and Pattern Recognition (34 citations), Artificial Intelligence (49 citations) and Hardware and Architecture (8 citations). K. Pedro has collaborated with scholars based in United States, Switzerland and Thailand. Frequent co-authors include O. Amram, M. Pierini, L. T. Le Pottier, F. Canelli, A. De Cosa, J. Niedziela, S. Jindariani, J. Ngadiuba, Nhan Viet Tran and Duc Hoang. Their work appears in journals such as Machine Learning Science and Technology, Journal of High Energy Physics, Physical review. D, SciPost Physics Core and Frontiers in Physics.
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.