Ben Day
Impact in
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- Computational Drug Discovery Methods
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- Advanced Graph Neural Networks
- Reinforcement Learning in Robotics
Papers in
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- Bioinformatics and Genomic Networks 2
- Protein Structure and Dynamics 2
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- Computational Drug Discovery Methods 2
- Co-authors
- Philip B. Meggs (1 shared paper)Rob Carter (1 shared paper)Píetro Lió (2 shared papers)Tom L. Blundell (2 shared papers)Arian R. Jamasb (2 shared papers)Cristian Bodnar (1 shared paper)Richard Vickers (1 shared paper)Cristian Regep (1 shared paper)
- Journals
- Briefings in Bioinformatics (1 paper)Methods in molecular biology (1 paper)Physical Review (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)CERN Document Server (European Organization for Nuclear Research) (1 paper)
- Partner nations
- United KingdomUnited StatesCanada
In The Last Decade
Ben Day
5 papers receiving 315 citations
Peers
Comparison fields: 5 of 100
- Computational Theory and Mathematics 103
- Artificial Intelligence 94
- Nuclear and High Energy Physics 30
- Health Informatics 3
- Condensed Matter Physics 19
Countries citing papers authored by Ben Day
This map shows the geographic impact of Ben Day'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 Ben Day with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ben Day more than expected).
Fields of papers citing papers by Ben Day
This network shows the impact of papers produced by Ben Day. 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 Ben Day. The network helps show where Ben Day may publish in the future.
Co-authors
The 16 scholars most cited alongside Ben Day, 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 | 2021 | 176 | |
| 2 | 1966 | 55 | |
| 3 | Typographic Design: Form and Communication | 1985 | 50 |
| 4 | 2020 | 43 | |
| 5 | 2021 | 14 |
About Ben Day
Ben Day is a scholar working on Molecular Biology, Computational Theory and Mathematics, Atomic and Molecular Physics, and Optics, Artificial Intelligence and Nuclear and High Energy Physics, having authored 5 papers that have together received 338 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (2 papers), Bioinformatics and Genomic Networks (2 papers), Protein Structure and Dynamics (2 papers), Evolutionary Algorithms and Applications (1 paper), Reinforcement Learning in Robotics (1 paper), Quantum Chromodynamics and Particle Interactions (1 paper), Robotic Locomotion and Control (1 paper) and Quantum, superfluid, helium dynamics (1 paper). The work is most often cited by research in Computational Theory and Mathematics (103 citations), Artificial Intelligence (94 citations), Nuclear and High Energy Physics (30 citations), Health Informatics (3 citations) and Condensed Matter Physics (19 citations). Ben Day has collaborated with scholars based in United Kingdom, United States and Canada. Frequent co-authors include Philip B. Meggs, Rob Carter, Píetro Lió, Tom L. Blundell, Arian R. Jamasb, Cristian Bodnar, Richard Vickers, Cristian Regep, Charles S. Roberts and Thomas Gaudelet. Their work appears in journals such as Briefings in Bioinformatics, Methods in molecular biology, Physical Review, Proceedings of the AAAI Conference on Artificial Intelligence and CERN Document Server (European Organization for Nuclear Research).
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.