Taras Kucherenko
- Computer Vision and Pattern Recognition top 10%
- Control and Systems Engineering top 10%
- Human-Computer Interaction top 5%
- Artificial Intelligence
- Social Psychology
- Co-authors
- Gustav Eje HenterJonas BeskowSimon AlexandersonHedvig KjellströmMichael NeffYoungwoo YoonDai HasegawaNaoshi Kaneko
- Topics
- Social Robot Interaction and HRI (8 papers)Human Motion and Animation (8 papers)Human Pose and Action Recognition (7 papers)
- Cited by
- Human-Computer InteractionComputer Vision and Pattern RecognitionControl and Systems Engineering
- Journals
- ACM Transactions on GraphicsComputer Graphics ForumInternational Journal of Human-Computer Interaction
- Partner nations
- SwedenSouth KoreaNetherlands
In The Last Decade
Taras Kucherenko
15 papers receiving 245 citations
Peers
Comparison fields: 5 of 36
- Computer Vision and Pattern Recognition 136
- Control and Systems Engineering 128
- Human-Computer Interaction 96
- Artificial Intelligence 85
- Social Psychology 65
Countries citing papers authored by Taras Kucherenko
This map shows the geographic impact of Taras Kucherenko'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 Taras Kucherenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Taras Kucherenko more than expected).
Fields of papers citing papers by Taras Kucherenko
This network shows the impact of papers produced by Taras Kucherenko. 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 Taras Kucherenko. The network helps show where Taras Kucherenko may publish in the future.
Co-authorship network of co-authors of Taras Kucherenko
This figure shows the co-authorship network connecting the top 25 collaborators of Taras Kucherenko. A scholar is included among the top collaborators of Taras Kucherenko 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 Taras Kucherenko. Taras Kucherenko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 1 | |
| 3 | 33 | |
| 4 | 38 | |
| 5 | 6 | |
| 6 | 1 | |
| 7 | 25 | |
| 8 | 13 | |
| 9 | 8 | |
| 10 | 97 | |
| 11 | 1 | |
| 12 | Should Beat Gestures Be Learned Or Designed? : A Benchmarking User Study | 5 |
| 13 | Learning Non-verbal Behavior for a Social Robot from YouTube Videos | 4 |
| 14 | A Neural Network Approach to Missing Marker Reconstruction. | 6 |
| 15 | 5 |
About Taras Kucherenko
Taras Kucherenko is a scholar working on Human-Computer Interaction, Social Psychology and Control and Systems Engineering, having authored 15 papers that have together received 252 indexed citations. Recurring topics across this work include Social Robot Interaction and HRI (8 papers), Human Motion and Animation (8 papers) and Human Pose and Action Recognition (7 papers). The work is most often cited by research in Human-Computer Interaction (96 citations), Computer Vision and Pattern Recognition (136 citations) and Control and Systems Engineering (128 citations). Taras Kucherenko has collaborated with scholars based in Sweden, South Korea and Netherlands. Frequent co-authors include Gustav Eje Henter, Jonas Beskow, Simon Alexanderson, Hedvig Kjellström, Michael Neff, Youngwoo Yoon, Dai Hasegawa, Naoshi Kaneko, Patrik Jonell and Chaitanya Ahuja. Their work appears in journals such as ACM Transactions on Graphics, Computer Graphics Forum and International Journal of Human-Computer Interaction.
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.