Tim Moon
Impact in
- Artificial Intelligence top 10%
- Stochastic Gradient Optimization Techniques
- Privacy-Preserving Technologies in Data
- Machine Learning and ELM
- Domain Adaptation and Few-Shot Learning
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- Advanced Neural Network Applications
Papers in
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- Stochastic Gradient Optimization Techniques 3
- Advanced Graph Neural Networks 2
- Topic Modeling 2
- Natural Language Processing Techniques 1
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- Advanced Neural Network Applications 4
- Co-authors
- Brian Van Essen (7 shared papers)Nikoli Dryden (4 shared papers)Sam Adé Jacobs (5 shared papers)Naoya Maruyama (2 shared papers)Marc Snir (2 shared papers)Andy Yoo (1 shared paper)Ian Karlin (2 shared papers)Derek Jones (1 shared paper)
- Journals
- The International Journal of High Performance Computing Applications (1 paper)Zenodo (CERN European Organization for Nuclear Research) (1 paper)IEEE International Conference on High Performance Computing, Data, and Analytics (1 paper)OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (1 paper)
- Partner nations
- United StatesJamaica
In The Last Decade
Tim Moon
9 papers receiving 216 citations
Peers
Comparison fields: 5 of 35
- Artificial Intelligence 164
- Computer Vision and Pattern Recognition 88
- Hardware and Architecture 25
- Computational Mathematics 2
- Computer Networks and Communications 51
Countries citing papers authored by Tim Moon
This map shows the geographic impact of Tim Moon'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 Tim Moon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Moon more than expected).
Fields of papers citing papers by Tim Moon
This network shows the impact of papers produced by Tim Moon. 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 Tim Moon. The network helps show where Tim Moon may publish in the future.
Co-authors
The 24 scholars most cited alongside Tim Moon, 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 | 2016 | 86 | |
| 2 | 2016 | 52 | |
| 3 | 2018 | 32 | |
| 4 | 2021 | 21 | |
| 5 | 2019 | 21 | |
| 6 | 2021 | 7 | |
| 7 | 2017 | 3 | |
| 8 | Predicting the Diagnosis of Type 2 Diabetes Using Electronic Medical Records | 2014 | 1 |
| 9 | 2022 | 1 |
About Tim Moon
Tim Moon is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Health Information Management and Computational Mechanics, having authored 9 papers that have together received 224 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Stochastic Gradient Optimization Techniques (3 papers), Advanced Graph Neural Networks (2 papers), Topic Modeling (2 papers), Machine Learning in Bioinformatics (1 paper), Natural Language Processing Techniques (1 paper), Artificial Intelligence in Healthcare (1 paper) and Computational Drug Discovery Methods (1 paper). The work is most often cited by research in Artificial Intelligence (164 citations), Computer Vision and Pattern Recognition (88 citations), Hardware and Architecture (25 citations), Computational Mathematics (2 citations) and Computer Networks and Communications (51 citations). Tim Moon has collaborated with scholars based in United States and Jamaica. Frequent co-authors include Brian Van Essen, Nikoli Dryden, Sam Adé Jacobs, Naoya Maruyama, Marc Snir, Andy Yoo, Ian Karlin, Derek Jones, Felice C. Lightstone and Jonathan Allen. Their work appears in journals such as The International Journal of High Performance Computing Applications, Zenodo (CERN European Organization for Nuclear Research), IEEE International Conference on High Performance Computing, Data, and Analytics and OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).
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.