Peter Sadowski
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- Particle physics theoretical and experimental studies 6
- Particle Detector Development and Performance 5
- High-Energy Particle Collisions Research 2
- Artificial Intelligence top 2%
- Computational Physics and Python Applications 7
- Neural Networks and Applications 6
- AI in cancer detection 4
- Computational Mathematics top 10%
- Health Informatics top 10%
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- Radiomics and Machine Learning in Medical Imaging 4
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- Sparse and Compressive Sensing Techniques 2
- Co-authors
- Pierre BaldiD. WhitesonDavide ChiccoKevin BauerZhiqin LuMichael SchiffJustin E. StopaAlexis Mouche
- Journals
- Neural Networks (3 papers)Physical review. D (2 papers)The Astrophysical Journal (2 papers)
- Partner nations
- United StatesSouth KoreaFrance
In The Last Decade
Peter Sadowski
38 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 173
- Nuclear and High Energy Physics 388
- Artificial Intelligence 765
- Computational Mathematics 9
- Computer Vision and Pattern Recognition 288
- Health Informatics 16
Countries citing papers authored by Peter Sadowski
This map shows the geographic impact of Peter Sadowski'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 Peter Sadowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Sadowski more than expected).
Fields of papers citing papers by Peter Sadowski
This network shows the impact of papers produced by Peter Sadowski. 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 Peter Sadowski. The network helps show where Peter Sadowski may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Peter Sadowski, 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 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 4 | |
| 7 | 2024 | 2 | |
| 8 | 2022 | 7 | |
| 9 | 2021 | 4 | |
| 10 | Deep Learning for Climate Models of the Atlantic Ocean. | 2020 | 0 |
| 11 | 2018 | 3 | |
| 12 | 2017 | 18 | |
| 13 | 2016 | 7 | |
| 14 | 2016 | 38 | |
| 15 | 2015 | 64 | |
| 16 | Deep learning, dark knowledge, and dark matter | 2014 | 15 |
| 17 | Searching for Higgs Boson Decay Modes with Deep Learning | 2014 | 9 |
| 18 | Searching for exotic particles in high-energy physics with deep learningbreakdown → | 2014 | 647 |
| 19 | 2014 | 121 | |
| 20 | Understanding Dropout | 2013 | 251 |
About Peter Sadowski
Peter Sadowski is a scholar working on Artificial Intelligence, Health Informatics, Nuclear and High Energy Physics, Astronomy and Astrophysics and Instrumentation, having authored 45 papers that have together received 1.9k indexed citations. Recurring topics across this work include Computational Physics and Python Applications (7 papers), Particle physics theoretical and experimental studies (6 papers), Neural Networks and Applications (6 papers), Particle Detector Development and Performance (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), AI in cancer detection (4 papers), Sparse and Compressive Sensing Techniques (2 papers) and High-Energy Particle Collisions Research (2 papers). The work is most often cited by research in Nuclear and High Energy Physics (388 citations), Artificial Intelligence (765 citations), Computational Mathematics (9 citations), Computer Vision and Pattern Recognition (288 citations) and Health Informatics (16 citations). Peter Sadowski has collaborated with scholars based in United States, South Korea and France. Frequent co-authors include Pierre Baldi, D. Whiteson, Davide Chicco, Kevin Bauer, Zhiqin Lu, Michael Schiff, Justin E. Stopa, Alexis Mouche, A. Søgaard and C. O. Shimmin. Their work appears in journals such as Neural Networks, Physical review. D, The Astrophysical Journal, Artificial Intelligence and Journal of Chemical Information and Modeling.
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.