Maria Shugrina
- Computer Vision and Pattern Recognition top 5%
- Computer Graphics and Computer-Aided Design top 5%
- Artificial Intelligence
- Computational Mechanics
- Social Psychology
- Co-authors
- Sanja FidlerMargrit BetkeWojciech MatusikAriel ShamirJohn CollomosseKaran SinghAmlan KarSameh Khamis
- Topics
- Computer Graphics and Visualization Techniques (7 papers)3D Shape Modeling and Analysis (5 papers)Advanced Vision and Imaging (4 papers)
- Cited by
- Computer Graphics and Computer-Aided DesignHuman-Computer InteractionComputer Vision and Pattern Recognition
- Journals
- ACM Transactions on Graphics2021 IEEE/CVF International Conference on Computer Vision (ICCV)View
- Partner nations
- CanadaUnited KingdomUnited States
In The Last Decade
Maria Shugrina
13 papers receiving 301 citations
Peers
Comparison fields: 5 of 57
- Computer Vision and Pattern Recognition 157
- Computer Graphics and Computer-Aided Design 69
- Artificial Intelligence 63
- Computational Mechanics 61
- Social Psychology 49
Countries citing papers authored by Maria Shugrina
This map shows the geographic impact of Maria Shugrina'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 Maria Shugrina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Shugrina more than expected).
Fields of papers citing papers by Maria Shugrina
This network shows the impact of papers produced by Maria Shugrina. 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 Maria Shugrina. The network helps show where Maria Shugrina may publish in the future.
Co-authorship network of co-authors of Maria Shugrina
This figure shows the co-authorship network connecting the top 25 collaborators of Maria Shugrina. A scholar is included among the top collaborators of Maria Shugrina 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 Maria Shugrina. Maria Shugrina is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 1 | |
| 3 | 6 | |
| 4 | 43 | |
| 5 | 18 | |
| 6 | 8 | |
| 7 | 28 | |
| 8 | 52 | |
| 9 | 24 | |
| 10 | 39 | |
| 11 | Formatting Time-Aligned ASR Transcripts for Readability | 18 |
| 12 | 47 | |
| 13 | 38 |
About Maria Shugrina
Maria Shugrina is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 13 papers that have together received 326 indexed citations. Recurring topics across this work include Computer Graphics and Visualization Techniques (7 papers), 3D Shape Modeling and Analysis (5 papers) and Advanced Vision and Imaging (4 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (69 citations), Human-Computer Interaction (45 citations) and Computer Vision and Pattern Recognition (157 citations). Maria Shugrina has collaborated with scholars based in Canada, United Kingdom and United States. Frequent co-authors include Sanja Fidler, Margrit Betke, Wojciech Matusik, Ariel Shamir, John Collomosse, Karan Singh, Amlan Kar, Sameh Khamis, Kangxue Yin and Jun Gao. Their work appears in journals such as ACM Transactions on Graphics, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and View.
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.