Matthew Kay

5.0k citations
103 papers · 2.6k indexed · 1 hit paper · h-index 26

Matthew Kay

100 papers receiving 2.5k citations

Hit Papers

Unequal Representation and Gender Stereotypes in Image Se...253201520262018202250100150200250

Peers

Matthew Kay
Comparison fields: 5 of 166
  • Human-Computer Interaction 502
  • Applied Psychology 243
  • General Decision Sciences 65
  • Computer Vision and Pattern Recognition 713
  • Experimental and Cognitive Psychology 366
Replace Shlomo Berkovsky with:
Shlomo Berkovsky Australia
Matt Jones United Kingdom
Joseph Jay Williams United States
Michael Terry Canada
Justin G. Hollands Canada
Anna L. Cox United Kingdom
Jessica Hullman United States
Bruno Lepri Italy
Emiel Krahmer Netherlands
Alasdair D. F. Clarke United Kingdom
Matthew Kay relative to Shlomo Berkovsky Australia Shlomo Berkovsky's profile →
Citations per field
00.5×3.2×
Shlomo Berkovsky · 1×
Citations per year

Countries citing papers authored by Matthew Kay

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Kay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Matthew Kay, 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 Matthew Kay Line = papers co-authored together Matthew Kay links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20243
2 20245
3 20246
4 20243
5 20241
6 20231
7 202312
8 202135
9
Visual Reasoning Strategies and Satisficing: How Uncertainty Visualization Design Impacts Effect Size Judgments and Decisions.
20201
10 202015
11 20197
12 201814
13 20188
14 201817
15 20161
16 2015151
17 20144
18 201343
19 20131
20
A guardian angel.
20002

About Matthew Kay

Matthew Kay is a scholar working on Human-Computer Interaction, General Decision Sciences and Computer Vision and Pattern Recognition, having authored 103 papers that have together received 2.6k indexed citations. Recurring topics across this work include Data Visualization and Analytics (32 papers), Radiation Effects in Electronics (17 papers), Innovative Human-Technology Interaction (14 papers), Semiconductor materials and devices (13 papers), Data Analysis with R (12 papers), Advanced Memory and Neural Computing (9 papers), Mobile Health and mHealth Applications (7 papers) and Green IT and Sustainability (6 papers). The work is most often cited by research in Human-Computer Interaction (502 citations), Applied Psychology (243 citations) and General Decision Sciences (65 citations). Matthew Kay has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Sean A. Munson, Julie A. Kientz, Jessica Hullman, Eun Kyoung Choe, Cynthia Matuszek, Nathaniel F. Watson, Alex Kale, Matthew J. Gadlage, Adam R. Duncan and Michael Terry. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Nuclear Science, IEEE Transactions on Device and Materials Reliability, Gait & Posture and Frontiers in Psychology.

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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