Mark Elshaw
Impact in
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- Emotion and Mood Recognition
- Automotive Engineering top 10%
- Autonomous Vehicle Technology and Safety
Papers in
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- Neural Networks and Applications 12
- Reinforcement Learning in Robotics 8
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- Embodied and Extended Cognition 9
- Motor Control and Adaptation 3
- Co-authors
- Stefan Wermter (23 shared papers)Vasile Palade (10 shared papers)Cornelius Weber (13 shared papers)Günther Palm (4 shared papers)Chitta Saha (2 shared papers)Stratis A. Kanarachos (2 shared papers)Sujan Rajbhandari (2 shared papers)M. Nazmul Huda (2 shared papers)
- Journals
- Connection Science (2 papers)Neural Networks (2 papers)Applied Sciences (1 paper)Robotics and Autonomous Systems (1 paper)Neural Computing and Applications (1 paper)
- Partner nations
- United KingdomGermanyItaly
In The Last Decade
Mark Elshaw
39 papers receiving 540 citations
Peers
Comparison fields: 5 of 67
- Experimental and Cognitive Psychology 173
- Automotive Engineering 50
- Social Psychology 98
- Cognitive Neuroscience 81
- Artificial Intelligence 134
Countries citing papers authored by Mark Elshaw
This map shows the geographic impact of Mark Elshaw'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 Elshaw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Elshaw more than expected).
Fields of papers citing papers by Mark Elshaw
This network shows the impact of papers produced by Mark Elshaw. 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 Elshaw. The network helps show where Mark Elshaw may publish in the future.
Co-authors
The 18 scholars most cited alongside Mark Elshaw, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 75 | |
| 2 | 2018 | 67 | |
| 3 | 2005 | 51 | |
| 4 | 2017 | 47 | |
| 5 | 2004 | 37 | |
| 6 | 2016 | 28 | |
| 7 | 1999 | 20 | |
| 8 | 2013 | 19 | |
| 9 | 2018 | 18 | |
| 10 | 2003 | 17 | |
| 11 | Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience | 2005 | 17 |
| 12 | 2005 | 16 | |
| 13 | 2020 | 14 | |
| 14 | 2016 | 12 | |
| 15 | 2005 | 10 | |
| 16 | 2018 | 10 | |
| 17 | 2005 | 9 | |
| 18 | 2006 | 9 | |
| 19 | 2001 | 8 | |
| 20 | 2008 | 8 |
About Mark Elshaw
Mark Elshaw is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Experimental and Cognitive Psychology, Social Psychology and Computer Vision and Pattern Recognition, having authored 39 papers that have together received 566 indexed citations. Recurring topics across this work include Neural Networks and Applications (12 papers), Emotion and Mood Recognition (10 papers), Embodied and Extended Cognition (9 papers), Reinforcement Learning in Robotics (8 papers), Social Robot Interaction and HRI (7 papers), Action Observation and Synchronization (7 papers), Robot Manipulation and Learning (4 papers) and Motor Control and Adaptation (3 papers). The work is most often cited by research in Experimental and Cognitive Psychology (173 citations), Automotive Engineering (50 citations), Social Psychology (98 citations), Cognitive Neuroscience (81 citations) and Artificial Intelligence (134 citations). Mark Elshaw has collaborated with scholars based in United Kingdom, Germany and Italy. Frequent co-authors include Stefan Wermter, Vasile Palade, Cornelius Weber, Günther Palm, Chitta Saha, Stratis A. Kanarachos, Sujan Rajbhandari, M. Nazmul Huda, Friedemann Pulvermüller A and Christo Panchev. Their work appears in journals such as Connection Science, Neural Networks, Applied Sciences, Robotics and Autonomous Systems and Neural Computing and Applications.
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.