James Requeima
Impact in
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- Species Distribution and Climate Change
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- Domain Adaptation and Few-Shot Learning
- Gaussian Processes and Bayesian Inference
Papers in
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- Gaussian Processes and Bayesian Inference 3
- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Data Classification 1
- Machine Learning and Algorithms 1
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- Meteorological Phenomena and Simulations 2
- Co-authors
- Richard E. Turner (6 shared papers)Jonathan Gordon (3 shared papers)Sebastian Nowozin (2 shared papers)J. Scott Hosking (2 shared papers)Tom R. Andersson (2 shared papers)Michael Herzog (1 shared paper)Matthew A. Lazzara (1 shared paper)Matthew Chantry (1 shared paper)
- Journals
- Nature (1 paper)SHILAP Revista de lepidopterología (1 paper)Cambridge University Engineering Department Publications Database (1 paper)Apollo (University of Cambridge) (3 papers)
- Partner nations
- United KingdomUnited StatesCanada
In The Last Decade
James Requeima
6 papers receiving 39 citations
Peers
Comparison fields: 5 of 25
- Ecological Modeling 4
- Artificial Intelligence 21
- Computer Vision and Pattern Recognition 13
- Atmospheric Science 10
- 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 14 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 | 10 | |
| 2 | 2019 | 10 | |
| 3 | 2023 | 7 | |
| 4 | 2019 | 6 | |
| 5 | 2020 | 6 | |
| 6 | Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes | 2020 | 1 |
| 7 | 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 7 papers that have together received 40 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (3 papers), Meteorological Phenomena and Simulations (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Machine Learning and Data Classification (1 paper), Time Series Analysis and Forecasting (1 paper), Air Quality Monitoring and Forecasting (1 paper), Machine Learning and Algorithms (1 paper) and Cancer-related molecular mechanisms research (1 paper). The work is most often cited by research in Ecological Modeling (4 citations), Artificial Intelligence (21 citations), Computer Vision and Pattern Recognition (13 citations), Atmospheric Science (10 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, Jonathan Gordon, Sebastian Nowozin, J. Scott Hosking, Tom R. Andersson, Michael Herzog, Matthew A. Lazzara, Matthew Chantry, Daniel C. Jones and Nicholas D. Lane. Their work appears in journals such as Nature, SHILAP Revista de lepidopterología, Cambridge University Engineering Department Publications Database 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.