J. Austin
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Signal Processing top 10%
- Information Systems
- Computer Networks and Communications
- Co-authors
- Joanna HodgeNick PearsSimon HickinbothamTom JacksonMarty FletcherMichael WeeksBenjamin LiangMark Jessop
- Topics
- Neural Networks and Applications (8 papers)Face and Expression Recognition (4 papers)Advanced Memory and Neural Computing (3 papers)
- Journals
- Applied Physics LettersProceedings of the IEEEIEEE Transactions on Knowledge and Data Engineering
- Partner nations
- United KingdomCanadaUnited States
In The Last Decade
J. Austin
31 papers receiving 229 citations
Peers
Comparison fields: 5 of 63
- Artificial Intelligence 124
- Computer Vision and Pattern Recognition 106
- Signal Processing 52
- Information Systems 36
- Computer Networks and Communications 35
Countries citing papers authored by J. Austin
This map shows the geographic impact of J. Austin'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 J. Austin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Austin more than expected).
Fields of papers citing papers by J. Austin
This network shows the impact of papers produced by J. Austin. 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 J. Austin. The network helps show where J. Austin may publish in the future.
Co-authorship network of co-authors of J. Austin
This figure shows the co-authorship network connecting the top 25 collaborators of J. Austin. A scholar is included among the top collaborators of J. Austin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with J. Austin. J. Austin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case | 1 |
| 2 | 9 | |
| 3 | 7 | |
| 4 | The CARMEN Neuroscience Server | 4 |
| 5 | 3 | |
| 6 | 2 | |
| 7 | 1 | |
| 8 | 19 | |
| 9 | 46 | |
| 10 | 1 | |
| 11 | 3 | |
| 12 | 1 | |
| 13 | 0 | |
| 14 | 2 | |
| 15 | 8 | |
| 16 | 4 | |
| 17 | 2 | |
| 18 | 6 | |
| 19 | 2 | |
| 20 | ADAM: an associative neural architecture for invariant pattern classification | 3 |
About J. Austin
J. Austin is a scholar working on Health Informatics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 35 papers that have together received 258 indexed citations. Recurring topics across this work include Neural Networks and Applications (8 papers), Face and Expression Recognition (4 papers) and Advanced Memory and Neural Computing (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (106 citations), Signal Processing (52 citations) and Artificial Intelligence (124 citations). J. Austin has collaborated with scholars based in United Kingdom, Canada and United States. Frequent co-authors include Joanna Hodge, Nick Pears, Simon Hickinbotham, Tom Jackson, Marty Fletcher, Michael Weeks, Benjamin Liang, Mark Jessop, Richard A. Davis and Alain Nogaret. Their work appears in journals such as Applied Physics Letters, Proceedings of the IEEE and IEEE Transactions on Knowledge and Data Engineering.
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.