Alexander Titterton
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- Particle physics theoretical and experimental studies 2
- Black Holes and Theoretical Physics 1
- High-Energy Particle Collisions Research 1
- Particle Detector Development and Performance 1
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- Advanced Neural Network Applications 1
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- Photoreceptor and optogenetics research 1
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- Machine Learning in Materials Science 1
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- Parallel Computing and Optimization Techniques 1
- Co-authors
- Stefano MorettiUlrich EllwangerC. H. Shepherd-ThemistocleousH. U. FlaecherK. PetridisA. M. MarshallSamuel Maddrell-ManderL. R. Madhan Mohan
- Journals
- SHILAP Revista de lepidopterología (1 paper)Journal of High Energy Physics (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United KingdomHong KongGermany
In The Last Decade
Alexander Titterton
3 papers receiving 15 citations
Peers
Comparison fields: 5 of 11
- Nuclear and High Energy Physics 8
- Numerical Analysis 1
- Cognitive Neuroscience 2
- Computer Vision and Pattern Recognition 2
- Statistical and Nonlinear Physics 1
Countries citing papers authored by Alexander Titterton
This map shows the geographic impact of Alexander Titterton'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 Alexander Titterton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Titterton more than expected).
Fields of papers citing papers by Alexander Titterton
This network shows the impact of papers produced by Alexander Titterton. 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 Alexander Titterton. The network helps show where Alexander Titterton may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Alexander Titterton, 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 | 2024 | 2 | |
| 2 | 2024 | 0 | |
| 3 | 2021 | 5 | |
| 4 | 2018 | 8 |
About Alexander Titterton
Alexander Titterton is a scholar working on Nuclear and High Energy Physics, Hardware and Architecture and Cellular and Molecular Neuroscience, having authored 4 papers that have together received 15 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (2 papers), Black Holes and Theoretical Physics (1 paper), Photoreceptor and optogenetics research (1 paper), High-Energy Particle Collisions Research (1 paper), Particle Detector Development and Performance (1 paper), Machine Learning in Materials Science (1 paper), Parallel Computing and Optimization Techniques (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Nuclear and High Energy Physics (8 citations), Numerical Analysis (1 citation) and Cognitive Neuroscience (2 citations). Alexander Titterton has collaborated with scholars based in United Kingdom, Hong Kong and Germany. Frequent co-authors include Stefano Moretti, Ulrich Ellwanger, C. H. Shepherd-Themistocleous, H. U. Flaecher, K. Petridis, A. M. Marshall, Samuel Maddrell-Mander, L. R. Madhan Mohan, J. H. Rademacker and D. P. O’Hanlon. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of High Energy Physics and Proceedings of the AAAI Conference on Artificial Intelligence.
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.