Kai-Min Chang

2.2k total citations · 1 hit paper
20 papers, 1.2k citations indexed

About

Kai-Min Chang is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Social Psychology. According to data from OpenAlex, Kai-Min Chang has authored 20 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Cognitive Neuroscience, 9 papers in Artificial Intelligence and 2 papers in Social Psychology. Recurrent topics in Kai-Min Chang's work include EEG and Brain-Computer Interfaces (6 papers), Neurobiology of Language and Bilingualism (5 papers) and Intelligent Tutoring Systems and Adaptive Learning (3 papers). Kai-Min Chang is often cited by papers focused on EEG and Brain-Computer Interfaces (6 papers), Neurobiology of Language and Bilingualism (5 papers) and Intelligent Tutoring Systems and Adaptive Learning (3 papers). Kai-Min Chang collaborates with scholars based in United States, Taiwan and Italy. Kai-Min Chang's co-authors include Marcel Adam Just, Tom M. Mitchell, Robert A. Mason, Svetlana V. Shinkareva, J. Andrew Carlson, Vicente L. Malave, Charles Kemp, Alan Jern, Jack Mostow and Haohan Wang and has published in prestigious journals such as Science, NeuroImage and Psychological Review.

In The Last Decade

Kai-Min Chang

19 papers receiving 1.1k citations

Hit Papers

Predicting Human Brain Activity Associated with the Meani... 2008 2026 2014 2020 2008 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Predicting Human Brain Activity Associated with the Meanings of Nouns
    2008 805 citations Tom M. Mitchell, Svetlana V. Shinkareva et al. Science profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kai-Min Chang United States 10 742 318 165 110 106 20 1.2k
Ben Hutchinson United States 22 1.2k 1.6× 694 2.2× 206 1.2× 160 1.5× 147 1.4× 70 2.5k
David Carmel United Kingdom 23 1.0k 1.4× 311 1.0× 268 1.6× 335 3.0× 181 1.7× 59 1.7k
Sandra Blakeslee United States 9 514 0.7× 242 0.8× 226 1.4× 137 1.2× 85 0.8× 30 1.3k
Hermann Ebbinghaus 4 502 0.7× 253 0.8× 161 1.0× 188 1.7× 56 0.5× 6 1.2k
Pim Haselager Netherlands 25 1.2k 1.6× 219 0.7× 441 2.7× 267 2.4× 47 0.4× 94 1.8k
Kenneth J. Kurtz United States 16 623 0.8× 240 0.8× 142 0.9× 275 2.5× 124 1.2× 66 1.4k
Leendert van Maanen Netherlands 26 1.4k 1.8× 289 0.9× 123 0.7× 286 2.6× 36 0.3× 82 1.9k
Daniel Kaiser Germany 25 1.3k 1.7× 112 0.4× 145 0.9× 251 2.3× 291 2.7× 118 2.0k
Richard A. Heath Australia 16 900 1.2× 419 1.3× 174 1.1× 284 2.6× 84 0.8× 46 1.8k
Michael A. Cohen United States 25 1.6k 2.1× 134 0.4× 282 1.7× 448 4.1× 163 1.5× 72 2.3k

Countries citing papers authored by Kai-Min Chang

Since Specialization
Citations

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

Fields of papers citing papers by Kai-Min Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kai-Min Chang. 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 Kai-Min Chang. The network helps show where Kai-Min Chang may publish in the future.

Co-authorship network of co-authors of Kai-Min Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Kai-Min Chang. A scholar is included among the top collaborators of Kai-Min Chang 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 Kai-Min Chang. Kai-Min Chang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Chang, Kai-Min, Yen-Ju Lin, Chia‐Ling Wei, & Soon-Jyh Chang. (2020). Resistor-Based Temperature Sensing Chip with Digital Output. 1–4. 2 indexed citations
3.
Chang, Kai-Min, et al.. (2019). Metamorphosis from local knowledge to involuted disaster knowledge for disaster governance in a landslide-prone tribal community in Taiwan. International Journal of Disaster Risk Reduction. 42. 101339–101339. 37 indexed citations
4.
Jern, Alan, Kai-Min Chang, & Charles Kemp. (2018). Bayesian Belief Polarization. Figshare. 22. 853–861. 7 indexed citations
5.
Murphy, Brian, Kai-Min Chang, & Guillermo Cecchi. (2016). Machine Learning and Interpretation in Neuroimaging. Lecture notes in computer science. 1 indexed citations
6.
Wang, Jing, et al.. (2016). Identifying thematic roles from neural representations measured by functional magnetic resonance imaging. Cognitive Neuropsychology. 33(3-4). 257–264. 24 indexed citations
7.
Li, Xuanchong, et al.. (2015). Massive Open Online Proctor. 1129–1137. 33 indexed citations
8.
Xu, Yanbo, et al.. (2014). Using EEG in Knowledge Tracing. Educational Data Mining. 361–362. 2 indexed citations
9.
Jern, Alan, Kai-Min Chang, & Charles Kemp. (2014). Belief polarization is not always irrational.. Psychological Review. 121(2). 206–224. 110 indexed citations
10.
Chang, Kai-Min, et al.. (2014). Toward unobtrusive measurement of reading comprehension using low-cost EEG. 54–58. 12 indexed citations
11.
Wang, Haohan, et al.. (2013). Using EEG to Improve Massive Open Online Courses Feedback Interaction.. 52 indexed citations
12.
Chang, Kai-Min, et al.. (2013). Toward Exploiting EEG Input in a Reading Tutor. International Journal of Artificial Intelligence in Education. 22(1-2). 19–38. 33 indexed citations
13.
Chen, Yun-Nung, Kai-Min Chang, & Jack Mostow. (2012). Towards Using EEG to Improve ASR Accuracy. North American Chapter of the Association for Computational Linguistics. 382–385. 4 indexed citations
14.
Chang, Kai-Min. (2012). Quantitative Modeling of the Neural Representation of Nouns and Phrases. 2 indexed citations
15.
Chen, Yun-Nung, et al.. (2012). NeuroDialog. 65–66. 2 indexed citations
16.
Murphy, Brian, Kai-Min Chang, & Anna Korhonen. (2010). Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics. Research Portal (Queen's University Belfast). 70–78. 12 indexed citations
17.
Chang, Kai-Min, et al.. (2010). Quantitative modeling of the neural representation of objects: How semantic feature norms can account for fMRI activation. NeuroImage. 56(2). 716–727. 46 indexed citations
18.
Kemp, Charles, Kai-Min Chang, & Luigi Lombardi. (2010). Category and feature identification. Acta Psychologica. 133(3). 216–233. 7 indexed citations
19.
Chang, Kai-Min, Vladimir L. Cherkassky, Tom M. Mitchell, & Marcel Adam Just. (2009). Quantitative modeling of the neural representation of adjective-noun phrases to account for fMRI activation. 2. 638–638. 9 indexed citations
20.
Mitchell, Tom M., Svetlana V. Shinkareva, J. Andrew Carlson, et al.. (2008). Predicting Human Brain Activity Associated with the Meanings of Nouns. Science. 320(5880). 1191–1195. 805 indexed citations breakdown →

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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