Ben Day
Impact in
-
- Computational Drug Discovery Methods
-
- Advanced Graph Neural Networks
- Reinforcement Learning in Robotics
Papers in
-
- Bioinformatics and Genomic Networks 2
- Protein Structure and Dynamics 2
-
- Computational Drug Discovery Methods 2
- Co-authors
- Rob Carter (1 shared paper)Philip B. Meggs (1 shared paper)Píetro Lió (2 shared papers)Cristian Bodnar (1 shared paper)Tom L. Blundell (2 shared papers)Arian R. Jamasb (2 shared papers)Richard Vickers (1 shared paper)David Roblin (1 shared paper)
- Journals
- Briefings in Bioinformatics (1 paper)Methods in molecular biology (1 paper)CERN Document Server (European Organization for Nuclear Research) (1 paper)Physical Review (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United KingdomCanadaUnited States
In The Last Decade
Ben Day
5 papers receiving 285 citations
Peers
Comparison fields: 5 of 96
- Computational Theory and Mathematics 91
- Artificial Intelligence 82
- Nuclear and High Energy Physics 30
- Condensed Matter Physics 18
- Health Informatics 2
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 15 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 | 154 | |
| 2 | 1966 | 54 | |
| 3 | Typographic Design: Form and Communication | 1985 | 44 |
| 4 | 2020 | 38 | |
| 5 | 2021 | 12 |
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 302 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (2 papers), Computational Drug Discovery Methods (2 papers), Protein Structure and Dynamics (2 papers), Quantum Chromodynamics and Particle Interactions (1 paper), Evolutionary Algorithms and Applications (1 paper), Quantum, superfluid, helium dynamics (1 paper), Reinforcement Learning in Robotics (1 paper) and Quantum many-body systems (1 paper). The work is most often cited by research in Computational Theory and Mathematics (91 citations), Artificial Intelligence (82 citations), Nuclear and High Energy Physics (30 citations), Condensed Matter Physics (18 citations) and Health Informatics (2 citations). Ben Day has collaborated with scholars based in United Kingdom, Canada and United States. Frequent co-authors include Rob Carter, Philip B. Meggs, Píetro Lió, Cristian Bodnar, Tom L. Blundell, Arian R. Jamasb, Richard Vickers, David Roblin, Michael M. Bronstein and Cristian Regep. Their work appears in journals such as Briefings in Bioinformatics, Methods in molecular biology, CERN Document Server (European Organization for Nuclear Research), Physical Review 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.