Mitch Wilkes
- Computer Vision and Pattern Recognition top 2%
- Experimental and Cognitive Psychology top 5%
- Signal Processing top 5%
- Artificial Intelligence top 5%
- Social Psychology top 10%
- Co-authors
- Marian NeamtuHakan ÇevıkalpAtalay BarkanaDaniel J. FranceRichard ShiaviStephen E. SilvermanMarilyn K. SilvermanK. Kawamura
- Topics
- Gaze Tracking and Assistive Technology (3 papers)Robotics and Automated Systems (3 papers)AI-based Problem Solving and Planning (3 papers)
- Cited by
- Signal ProcessingExperimental and Cognitive PsychologyComputer Vision and Pattern Recognition
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Biomedical EngineeringInternational Journal of Human-Computer Studies
- Partner nations
- United StatesTürkiyeChina
In The Last Decade
Mitch Wilkes
20 papers receiving 882 citations
Peers
Comparison fields: 5 of 96
- Computer Vision and Pattern Recognition 431
- Experimental and Cognitive Psychology 295
- Signal Processing 252
- Artificial Intelligence 215
- Social Psychology 113
Countries citing papers authored by Mitch Wilkes
This map shows the geographic impact of Mitch Wilkes'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 Mitch Wilkes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mitch Wilkes more than expected).
Fields of papers citing papers by Mitch Wilkes
This network shows the impact of papers produced by Mitch Wilkes. 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 Mitch Wilkes. The network helps show where Mitch Wilkes may publish in the future.
Co-authorship network of co-authors of Mitch Wilkes
This figure shows the co-authorship network connecting the top 25 collaborators of Mitch Wilkes. A scholar is included among the top collaborators of Mitch Wilkes 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 Mitch Wilkes. Mitch Wilkes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 12 | |
| 2 | 3 | |
| 3 | 68 | |
| 4 | 50 | |
| 5 | 350 | |
| 6 | 2 | |
| 7 | 7 | |
| 8 | 9 | |
| 9 | 0 | |
| 10 | 37 | |
| 11 | 1 | |
| 12 | 7 | |
| 13 | 3 | |
| 14 | 2 | |
| 15 | 9 | |
| 16 | 13 | |
| 17 | 6 | |
| 18 | 3 | |
| 19 | 350 | |
| 20 | Human-robot interaction methodology | 9 |
About Mitch Wilkes
Mitch Wilkes is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 21 papers that have together received 943 indexed citations. Recurring topics across this work include Gaze Tracking and Assistive Technology (3 papers), Robotics and Automated Systems (3 papers) and AI-based Problem Solving and Planning (3 papers). The work is most often cited by research in Signal Processing (252 citations), Experimental and Cognitive Psychology (295 citations) and Computer Vision and Pattern Recognition (431 citations). Mitch Wilkes has collaborated with scholars based in United States, Türkiye and China. Frequent co-authors include Marian Neamtu, Hakan Çevıkalp, Atalay Barkana, Daniel J. France, Richard Shiavi, Stephen E. Silverman, Marilyn K. Silverman, K. Kawamura, Ronald M. Salomon and Xiaochun Wang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Biomedical Engineering and International Journal of Human-Computer Studies.
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.