Michael Burch

4.3k citations
159 papers · 2.7k · h-index 25

Impact in

Papers in

Michael Burch

144 papers receiving 2.5k citations

Peers

Michael Burch
Comparison fields: 5 of 118
  • Human-Computer Interaction 674
  • Computer Vision and Pattern Recognition 2.0k
  • Statistical and Nonlinear Physics 531
  • Signal Processing 415
  • Geography, Planning and Development 192
Replace Benjamin Bach with:
Benjamin Bach United Kingdom
Pierre Dragicevic France
Tim Dwyer Australia
Alex Endert United States
Remco Chang United States
Petra Isenberg France
Michael Sedlmair Germany
Kim Marriott Australia
Zhicheng Liu United States
Melanie Tory Canada
Michael Burch relative to Benjamin Bach United Kingdom Benjamin Bach's profile →
Citations per field
00.5×4.2×
Benjamin Bach · 1×
Citations per year

Countries citing papers authored by Michael Burch

Since Specialization
Citations

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

Fields of papers citing papers by Michael Burch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Burch. 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 Michael Burch. The network helps show where Michael Burch may publish in the future.

Co-authors

The 25 scholars most cited alongside Michael Burch, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Burch Line = papers co-authored together Michael Burch links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 159 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016214
2 2017187
3 2012147
4 2014145
5 2014135
6 2011123
7 201199
8 201668
9 201565
10 200863
11 200962
12 201560
13 202256
14 200855
15 201453
16 201347
17 200945
18 201342
19 201237
20 201436

About Michael Burch

Michael Burch is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction, Signal Processing, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 159 papers that have together received 2.7k indexed citations. Recurring topics across this work include Data Visualization and Analytics (138 papers), Gaze Tracking and Assistive Technology (39 papers), Data Management and Algorithms (33 papers), Video Analysis and Summarization (24 papers), Complex Network Analysis Techniques (24 papers), Advanced Text Analysis Techniques (23 papers), Multimedia Communication and Technology (19 papers) and Geographic Information Systems Studies (17 papers). The work is most often cited by research in Human-Computer Interaction (674 citations), Computer Vision and Pattern Recognition (2.0k citations), Statistical and Nonlinear Physics (531 citations), Signal Processing (415 citations) and Geography, Planning and Development (192 citations). Michael Burch has collaborated with scholars based in Germany, Netherlands and United States. Frequent co-authors include Daniel Weiskopf, Stephan Diehl, Fabian Beck, Kuno Kurzhals, Tanja Blascheck, Thomas Ertl, Michael Raschke, Marcel Hlawatsch, Gennady Andrienko and Natalia Andrienko. Their work appears in journals such as Computer Graphics Forum, IEEE Transactions on Visualization and Computer Graphics, Information Visualization, Multimedia Tools and Applications and Computing in Science & Engineering.

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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