David V. Gealy

740 citations
7 papers · 465 indexed · h-index 7
Topics
Soft Robotics and Applications (5 papers)Robot Manipulation and Learning (4 papers)Surgical Simulation and Training (2 papers)
Journals
Zenodo (CERN European Organization for Nuclear Research)
Partner nations
United States

In The Last Decade

David V. Gealy

7 papers receiving 441 citations

Peers

David V. Gealy
Comparison fields: 5 of 61
  • Control and Systems Engineering 298
  • Biomedical Engineering 277
  • Computer Vision and Pattern Recognition 139
  • Surgery 81
  • Artificial Intelligence 74
Replace Stephen McKinley with:
Stephen McKinley United States
Giuseppe Andrea Fontanelli Italy
Saïd Zeghloul France
R. Koeppe Germany
Adnan Munawar United States
Yanlong Huang Italy
Vishal Satish United States
Adithyavairavan Murali United States
Martin Stommel New Zealand
Daniel Kubus Germany
David V. Gealy relative to Stephen McKinley United States Stephen McKinley's profile →
Citations per field
00.5×1.5×
Stephen McKinley · 1×
Citations per year

Countries citing papers authored by David V. Gealy

Since Specialization
Citations

This map shows the geographic impact of David V. Gealy'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 David V. Gealy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David V. Gealy more than expected).

Fields of papers citing papers by David V. Gealy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David V. Gealy. 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 David V. Gealy. The network helps show where David V. Gealy may publish in the future.

Co-authorship network of co-authors of David V. Gealy

This figure shows the co-authorship network connecting the top 25 collaborators of David V. Gealy. A scholar is included among the top collaborators of David V. Gealy based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David V. Gealy. David V. Gealy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

7 of 7 papers shown
#WorkIndexed citations
1 10
2 209
3 33
4 119
5 23
6 54
7 17

About David V. Gealy

David V. Gealy is a scholar working on Control and Systems Engineering, Biomedical Engineering and Soil Science, having authored 7 papers that have together received 465 indexed citations. Recurring topics across this work include Soft Robotics and Applications (5 papers), Robot Manipulation and Learning (4 papers) and Surgical Simulation and Training (2 papers). The work is most often cited by research in Control and Systems Engineering (298 citations), Human-Computer Interaction (39 citations) and Computer Vision and Pattern Recognition (139 citations). David V. Gealy has collaborated with scholars based in United States. Frequent co-authors include Ken Goldberg, Jeffrey Mahler, Xinyu Liu, Matthew Matl, Albert P. Li, Stephen McKinley, Animesh Garg, Siddarth Sen, Anca D. Dragan and Michael Laskey. Their work appears in journals such as Zenodo (CERN European Organization for Nuclear Research).

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.

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