Daniel Omeiza
Impact in
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- Autonomous Vehicle Technology and Safety
Papers in
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- Explainable Artificial Intelligence (XAI) 8
- Topic Modeling 2
- Natural Language Processing Techniques 2
- Adversarial Robustness in Machine Learning 1
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- Ethics and Social Impacts of AI 4
- Co-authors
- Lars Kunze (8 shared papers)Marina Jirotka (3 shared papers)Lili Jiang (1 shared paper)Matthew Gadd (1 shared paper)Shuyang Sun (1 shared paper)Bo Zhao (1 shared paper)Daniele De Martini (1 shared paper)Paul Newman (1 shared paper)
- Journals
- Transportation Research Part F Traffic Psychology and Behaviour (1 paper)Image and Vision Computing (1 paper)Lirias (KU Leuven) (1 paper)Oxford University Research Archive (ORA) (University of Oxford) (3 papers)2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (1 paper)
- Partner nations
- United KingdomUnited StatesSweden
In The Last Decade
Daniel Omeiza
11 papers receiving 108 citations
Peers
Comparison fields: 5 of 38
- Health Informatics 6
- Automotive Engineering 26
- Artificial Intelligence 58
- Social Psychology 27
- Safety Research 9
Countries citing papers authored by Daniel Omeiza
This map shows the geographic impact of Daniel Omeiza'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 Daniel Omeiza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Omeiza more than expected).
Fields of papers citing papers by Daniel Omeiza
This network shows the impact of papers produced by Daniel Omeiza. 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 Daniel Omeiza. The network helps show where Daniel Omeiza may publish in the future.
Co-authors
The 20 scholars most cited alongside Daniel Omeiza, 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 | 24 | |
| 2 | 2021 | 24 | |
| 3 | 2024 | 16 | |
| 4 | 2021 | 12 | |
| 5 | 2021 | 10 | |
| 6 | 2023 | 7 | |
| 7 | 2021 | 6 | |
| 8 | 2022 | 4 | |
| 9 | 2023 | 4 | |
| 10 | 2025 | 2 | |
| 11 | 2021 | 2 |
About Daniel Omeiza
Daniel Omeiza is a scholar working on Artificial Intelligence, Safety Research, Social Psychology, Automotive Engineering and Clinical Psychology, having authored 11 papers that have together received 111 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (8 papers), Ethics and Social Impacts of AI (4 papers), Human-Automation Interaction and Safety (3 papers), Topic Modeling (2 papers), Natural Language Processing Techniques (2 papers), Neuroethics, Human Enhancement, Biomedical Innovations (1 paper), Autonomous Vehicle Technology and Safety (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Health Informatics (6 citations), Automotive Engineering (26 citations), Artificial Intelligence (58 citations), Social Psychology (27 citations) and Safety Research (9 citations). Daniel Omeiza has collaborated with scholars based in United Kingdom, United States and Sweden. Frequent co-authors include Lars Kunze, Marina Jirotka, Lili Jiang, Matthew Gadd, Shuyang Sun, Bo Zhao, Daniele De Martini, Paul Newman, Seyun Kim and Malte Jung. Their work appears in journals such as Transportation Research Part F Traffic Psychology and Behaviour, Image and Vision Computing, Lirias (KU Leuven), Oxford University Research Archive (ORA) (University of Oxford) and 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI).
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.