Brian Thorne
Impact in
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- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Adversarial Robustness in Machine Learning
Papers in
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- Privacy-Preserving Technologies in Data 2
- Cryptography and Data Security 1
- Computational Physics and Python Applications 1
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- Simulation Techniques and Applications 2
- Co-authors
- Arik Friedman (1 shared paper)Stephen Hardy (2 shared papers)Roksana Boreli (1 shared paper)Raphaël Grasset (1 shared paper)Wilko Henecka (1 shared paper)Giorgio Patrini (1 shared paper)Richard Nock (1 shared paper)Hamish Ivey-Law (1 shared paper)
- Journals
- University of Canterbury Research Repository (University of Canterbury) (2 papers)
- Partner nations
- AustraliaNew ZealandUnited States
In The Last Decade
Brian Thorne
5 papers receiving 22 citations
Peers
Comparison fields: 5 of 17
- Health Informatics 1
- Artificial Intelligence 16
- Management Science and Operations Research 4
- Information Systems and Management 2
- Public Health, Environmental and Occupational Health 8
Countries citing papers authored by Brian Thorne
This map shows the geographic impact of Brian Thorne'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 Brian Thorne with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Thorne more than expected).
Fields of papers citing papers by Brian Thorne
This network shows the impact of papers produced by Brian Thorne. 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 Brian Thorne. The network helps show where Brian Thorne may publish in the future.
Co-authors
The 8 scholars most cited alongside Brian Thorne, 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 | 2014 | 15 | |
| 2 | The Impact of Record Linkage on Learning from Feature Partitioned Data | 2021 | 3 |
| 3 | ScipySim: Towards Distributed Heterogeneous System Simulation for the SciPy Platform | 2011 | 2 |
| 4 | 2011 | 2 | |
| 5 | Python for Prototyping Computer Vision Applications | 2010 | 1 |
About Brian Thorne
Brian Thorne is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Networks and Communications, Information Systems and Management and Computer Vision and Pattern Recognition, having authored 5 papers that have together received 23 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (2 papers), Scientific Computing and Data Management (2 papers), Distributed and Parallel Computing Systems (2 papers), Simulation Techniques and Applications (2 papers), Cancer Genomics and Diagnostics (1 paper), Cryptography and Data Security (1 paper), Computational Physics and Python Applications (1 paper) and Image Processing and 3D Reconstruction (1 paper). The work is most often cited by research in Health Informatics (1 citation), Artificial Intelligence (16 citations), Management Science and Operations Research (4 citations), Information Systems and Management (2 citations) and Public Health, Environmental and Occupational Health (8 citations). Brian Thorne has collaborated with scholars based in Australia, New Zealand and United States. Frequent co-authors include Arik Friedman, Stephen Hardy, Roksana Boreli, Raphaël Grasset, Wilko Henecka, Giorgio Patrini, Richard Nock and Hamish Ivey-Law. Their work appears in journals such as University of Canterbury Research Repository (University of Canterbury).
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.