Michael Correll
Impact in
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- Data Visualization and Analytics
- Video Analysis and Summarization
- Image and Video Quality Assessment
- General Decision Sciences top 10%
Papers in ⓘ
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- Data Visualization and Analytics 31
- Image and Video Quality Assessment 4
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- Data Analysis with R 12
- Co-authors
- Michael Gleicher (15 shared papers)Jeffrey Heer (4 shared papers)Alper Sarıkaya (3 shared papers)Melanie Tory (2 shared papers)Lyn Bartram (1 shared paper)Danyel Fisher (1 shared paper)Danielle Albers (4 shared papers)Steven Franconeri (3 shared papers)
- Journals
- IEEE Transactions on Visualization and Computer Graphics (10 papers)Computer Graphics Forum (4 papers)Journal of Virology (2 papers)Journal of Vision (1 paper)Bioinformatics (1 paper)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Michael Correll
43 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 121
- Computer Vision and Pattern Recognition 750
- General Decision Sciences 41
- Signal Processing 124
- Human-Computer Interaction 60
- Artificial Intelligence 341
Countries citing papers authored by Michael Correll
This map shows the geographic impact of Michael Correll'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 Correll with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Correll more than expected).
Fields of papers citing papers by Michael Correll
This network shows the impact of papers produced by Michael Correll. 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 Correll. The network helps show where Michael Correll may publish in the future.
Co-authors
The 25 scholars most cited alongside Michael Correll, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 198 | |
| 2 | 2014 | 161 | |
| 3 | 2018 | 103 | |
| 4 | 2014 | 74 | |
| 5 | 2013 | 73 | |
| 6 | 2012 | 54 | |
| 7 | 2018 | 49 | |
| 8 | 2018 | 38 | |
| 9 | 2017 | 37 | |
| 10 | 2020 | 32 | |
| 11 | 2014 | 31 | |
| 12 | 2011 | 30 | |
| 13 | 2016 | 29 | |
| 14 | 2011 | 25 | |
| 15 | 2019 | 25 | |
| 16 | 2014 | 22 | |
| 17 | 2019 | 21 | |
| 18 | 2016 | 19 | |
| 19 | 2023 | 18 | |
| 20 | 2017 | 16 |
About Michael Correll
Michael Correll is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience, Signal Processing and Ecological Modeling, having authored 45 papers that have together received 1.2k indexed citations. Recurring topics across this work include Data Visualization and Analytics (31 papers), Data Analysis with R (12 papers), Aesthetic Perception and Analysis (4 papers), Species Distribution and Climate Change (4 papers), Image and Video Quality Assessment (4 papers), Statistics Education and Methodologies (3 papers), Multimedia Communication and Technology (3 papers) and Time Series Analysis and Forecasting (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (750 citations), General Decision Sciences (41 citations), Signal Processing (124 citations), Human-Computer Interaction (60 citations) and Artificial Intelligence (341 citations). Michael Correll has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Michael Gleicher, Jeffrey Heer, Alper Sarıkaya, Melanie Tory, Lyn Bartram, Danyel Fisher, Danielle Albers, Steven Franconeri, Matthew Kay and Jessica Hullman. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, Journal of Virology, Journal of Vision and Bioinformatics.
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.