James Codella
Impact in
- Artificial Intelligence top 5%
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
- Topic Modeling
- Advanced Graph Neural Networks
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- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
Papers in
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- Mobile Health and mHealth Applications 4
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- Context-Aware Activity Recognition Systems 3
- Multimodal Machine Learning Applications 1
- Co-authors
- Yunhui Guo (1 shared paper)Kate Saenko (1 shared paper)Leonid Karlinsky (1 shared paper)Rogério Feris (1 shared paper)Noel Codella (1 shared paper)Tajana Rosing (1 shared paper)John R. Smith (1 shared paper)Ching-Hua Chen (5 shared papers)
- Journals
- Journal of Behavioral Medicine (1 paper)IBM Journal of Research and Development (1 paper)Medical Decision Making (1 paper)American Journal of Infection Control (1 paper)IEEE Journal of Biomedical and Health Informatics (1 paper)
- Partner nations
- United States
In The Last Decade
James Codella
9 papers receiving 402 citations
Peers
Comparison fields: 5 of 78
- Artificial Intelligence 235
- Computer Vision and Pattern Recognition 128
- Media Technology 24
- Radiology, Nuclear Medicine and Imaging 41
- Infectious Diseases 30
Countries citing papers authored by James Codella
This map shows the geographic impact of James Codella'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 James Codella with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Codella more than expected).
Fields of papers citing papers by James Codella
This network shows the impact of papers produced by James Codella. 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 James Codella. The network helps show where James Codella may publish in the future.
Co-authors
The 25 scholars most cited alongside James Codella, 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 | 212 | |
| 2 | 2019 | 98 | |
| 3 | 2014 | 36 | |
| 4 | 2018 | 25 | |
| 5 | 2017 | 13 | |
| 6 | 2018 | 13 | |
| 7 | 2019 | 8 | |
| 8 | 2017 | 3 | |
| 9 | FoodKG Enabled Q&A Application. | 2019 | 2 |
| 10 | 2024 | 0 |
About James Codella
James Codella is a scholar working on General Health Professions, Computer Vision and Pattern Recognition, Artificial Intelligence, Physiology and Infectious Diseases, having authored 10 papers that have together received 410 indexed citations. Recurring topics across this work include Mobile Health and mHealth Applications (4 papers), Context-Aware Activity Recognition Systems (3 papers), Physical Activity and Health (3 papers), Infection Control and Ventilation (1 paper), Behavioral Health and Interventions (1 paper), Artificial Intelligence in Healthcare (1 paper), Multimodal Machine Learning Applications (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Artificial Intelligence (235 citations), Computer Vision and Pattern Recognition (128 citations), Media Technology (24 citations), Radiology, Nuclear Medicine and Imaging (41 citations) and Infectious Diseases (30 citations). James Codella has collaborated with scholars based in United States. Frequent co-authors include Yunhui Guo, Kate Saenko, Leonid Karlinsky, Rogério Feris, Noel Codella, Tajana Rosing, John R. Smith, Ching-Hua Chen, Deborah L. McGuinness and Yu Chen. Their work appears in journals such as Journal of Behavioral Medicine, IBM Journal of Research and Development, Medical Decision Making, American Journal of Infection Control and IEEE Journal of Biomedical and Health Informatics.
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.