Mark Edmonds
Impact in
- Human-Computer Interaction top 10%
- Hand Gesture Recognition Systems
Papers in
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- Hand Gesture Recognition Systems 3
-
- Explainable Artificial Intelligence (XAI) 5
- Reinforcement Learning in Robotics 3
- Bayesian Modeling and Causal Inference 3
- Domain Adaptation and Few-Shot Learning 2
- Co-authors
- Song‐Chun Zhu (11 shared papers)Yixin Zhu (10 shared papers)Hangxin Liu (5 shared papers)Siyuan Qi (6 shared papers)Brandon Anthony Rothrock (5 shared papers)Xu Xie (4 shared papers)Hongjing Lu (5 shared papers)Ying Nian Wu (2 shared papers)
- Journals
- Science Robotics (2 papers)Wear (2 papers)Cognitive Science (2 papers)Applied Energy (1 paper)Engineering (1 paper)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Mark Edmonds
18 papers receiving 427 citations
Peers
Comparison fields: 5 of 68
- Human-Computer Interaction 47
- Health Informatics 9
- Artificial Intelligence 195
- Control and Systems Engineering 131
- Safety Research 29
Countries citing papers authored by Mark Edmonds
This map shows the geographic impact of Mark Edmonds'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 Mark Edmonds with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Edmonds more than expected).
Fields of papers citing papers by Mark Edmonds
This network shows the impact of papers produced by Mark Edmonds. 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 Mark Edmonds. The network helps show where Mark Edmonds may publish in the future.
Co-authors
The 25 scholars most cited alongside Mark Edmonds, 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 | 105 | |
| 2 | 2020 | 91 | |
| 3 | 2022 | 59 | |
| 4 | 2017 | 53 | |
| 5 | 2017 | 48 | |
| 6 | 1977 | 23 | |
| 7 | 1980 | 18 | |
| 8 | 2020 | 14 | |
| 9 | 2015 | 12 | |
| 10 | 2021 | 9 | |
| 11 | Human Causal Transfer: Challenges for Deep Reinforcement Learning. | 2018 | 8 |
| 12 | 2018 | 6 | |
| 13 | Decomposing Human Causal Learning: Bottom-up Associative Learning and Top-down Schema Reasoning. | 2019 | 5 |
| 14 | 2018 | 5 | |
| 15 | 1978 | 3 | |
| 16 | 1980 | 2 | |
| 17 | 2020 | 1 | |
| 18 | 2015 | 1 | |
| 19 | 1982 | 1 |
About Mark Edmonds
Mark Edmonds is a scholar working on Human-Computer Interaction, Artificial Intelligence, Control and Systems Engineering, Mechanics of Materials and Cognitive Neuroscience, having authored 19 papers that have together received 464 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (5 papers), Adhesion, Friction, and Surface Interactions (4 papers), Robot Manipulation and Learning (4 papers), Reinforcement Learning in Robotics (3 papers), Bayesian Modeling and Causal Inference (3 papers), Hand Gesture Recognition Systems (3 papers), Child and Animal Learning Development (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Human-Computer Interaction (47 citations), Health Informatics (9 citations), Artificial Intelligence (195 citations), Control and Systems Engineering (131 citations) and Safety Research (29 citations). Mark Edmonds has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Song‐Chun Zhu, Yixin Zhu, Hangxin Liu, Siyuan Qi, Brandon Anthony Rothrock, Xu Xie, Hongjing Lu, Ying Nian Wu, S. Douglas Probert and Andrew Michael Jones. Their work appears in journals such as Science Robotics, Wear, Cognitive Science, Applied Energy and 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.