Heather Riley
Impact in
-
- COVID-19 epidemiological studies
Papers in
-
- Multimodal Machine Learning Applications 2
- Augmented Reality Applications 1
- Advanced Image and Video Retrieval Techniques 1
-
- Domain Adaptation and Few-Shot Learning 2
- AI-based Problem Solving and Planning 1
- Co-authors
- Mohan Sridharan (3 shared papers)Pamela J. McKenzie (1 shared paper)Thomas House (1 shared paper)Rosalind M. Eggo (1 shared paper)Lorenzo Pellis (1 shared paper)Seb Bacon (1 shared paper)A. Sarah Walker (1 shared paper)Koen B. Pouwels (1 shared paper)
- Journals
- Statistical Methods in Medical Research (1 paper)Annals of Mathematics and Artificial Intelligence (1 paper)Frontiers in Robotics and AI (1 paper)University of Canterbury Research Repository (University of Canterbury) (1 paper)
- Partner nations
- United KingdomNew Zealand
In The Last Decade
Heather Riley
5 papers receiving 41 citations
Peers
Comparison fields: 5 of 31
- Modeling and Simulation 7
- Health Informatics 1
- General Dentistry 1
- Architecture 1
- Computer Vision and Pattern Recognition 13
Countries citing papers authored by Heather Riley
This map shows the geographic impact of Heather Riley'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 Heather Riley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heather Riley more than expected).
Fields of papers citing papers by Heather Riley
This network shows the impact of papers produced by Heather Riley. 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 Heather Riley. The network helps show where Heather Riley may publish in the future.
Co-authors
The 8 scholars most cited alongside Heather Riley, 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 | 2019 | 18 | |
| 2 | 2022 | 12 | |
| 3 | 2020 | 5 | |
| 4 | Evaluation of Microsoft HoloLens Augmented Reality Technology as a construction checking tool | 2019 | 3 |
| 5 | 2018 | 3 |
About Heather Riley
Heather Riley is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Infectious Diseases, Health and Building and Construction, having authored 5 papers that have together received 41 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Augmented Reality Applications (1 paper), Vaccine Coverage and Hesitancy (1 paper), AI-based Problem Solving and Planning (1 paper), 3D Surveying and Cultural Heritage (1 paper), Advanced Image and Video Retrieval Techniques (1 paper) and COVID-19 epidemiological studies (1 paper). The work is most often cited by research in Modeling and Simulation (7 citations), Health Informatics (1 citation), General Dentistry (1 citation), Architecture (1 citation) and Computer Vision and Pattern Recognition (13 citations). Heather Riley has collaborated with scholars based in United Kingdom and New Zealand. Frequent co-authors include Mohan Sridharan, Pamela J. McKenzie, Thomas House, Rosalind M. Eggo, Lorenzo Pellis, Seb Bacon, A. Sarah Walker and Koen B. Pouwels. Their work appears in journals such as Statistical Methods in Medical Research, Annals of Mathematics and Artificial Intelligence, Frontiers in Robotics and AI and 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.