James Requeima
Impact in
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- Meteorological Phenomena and Simulations
- Tropical and Extratropical Cyclones Research
Papers in
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- Gaussian Processes and Bayesian Inference 3
- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Algorithms 1
- Machine Learning and Data Classification 1
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- Meteorological Phenomena and Simulations 2
- Co-authors
- Richard E. Turner (6 shared papers)Sebastian Nowozin (2 shared papers)Jonathan Gordon (3 shared papers)Nicholas D. Lane (1 shared paper)J. Scott Hosking (2 shared papers)Matthew Chantry (1 shared paper)Michael Herzog (1 shared paper)Tom R. Andersson (2 shared papers)
- Journals
- Nature (1 paper)NERC Open Research Archive (Natural Environment Research Council) (1 paper)Cambridge University Engineering Department Publications Database (1 paper)Figshare (1 paper)Apollo (University of Cambridge) (3 papers)
- Partner nations
- United KingdomUnited StatesCanada
In The Last Decade
James Requeima
7 papers receiving 51 citations
Peers
Comparison fields: 5 of 27
- Atmospheric Science 13
- Ecological Modeling 3
- Computer Vision and Pattern Recognition 15
- Artificial Intelligence 23
- Oceanography 4
Countries citing papers authored by James Requeima
This map shows the geographic impact of James Requeima'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 Requeima with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Requeima more than expected).
Fields of papers citing papers by James Requeima
This network shows the impact of papers produced by James Requeima. 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 Requeima. The network helps show where James Requeima may publish in the future.
Co-authors
The 17 scholars most cited alongside James Requeima, 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 | 18 | |
| 2 | 2019 | 11 | |
| 3 | 2023 | 8 | |
| 4 | 2020 | 7 | |
| 5 | 2019 | 6 | |
| 6 | Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes | 2020 | 1 |
| 7 | 2015 | 1 | |
| 8 | 2024 | 0 |
About James Requeima
James Requeima is a scholar working on Artificial Intelligence, Atmospheric Science, Global and Planetary Change, Control and Systems Engineering and Signal Processing, having authored 8 papers that have together received 52 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Meteorological Phenomena and Simulations (2 papers), Machine Learning and Algorithms (1 paper), Climate variability and models (1 paper), Spectroscopy and Chemometric Analyses (1 paper), Machine Learning and Data Classification (1 paper) and Air Quality Monitoring and Forecasting (1 paper). The work is most often cited by research in Atmospheric Science (13 citations), Ecological Modeling (3 citations), Computer Vision and Pattern Recognition (15 citations), Artificial Intelligence (23 citations) and Oceanography (4 citations). James Requeima has collaborated with scholars based in United Kingdom, United States and Canada. Frequent co-authors include Richard E. Turner, Sebastian Nowozin, Jonathan Gordon, Nicholas D. Lane, J. Scott Hosking, Matthew Chantry, Michael Herzog, Tom R. Andersson, Daniel C. Jones and Matthew A. Lazzara. Their work appears in journals such as Nature, NERC Open Research Archive (Natural Environment Research Council), Cambridge University Engineering Department Publications Database, Figshare and Apollo (University of Cambridge).
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.