Emily Mower
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- Emotion and Mood Recognition 15
- Multisensory perception and integration 4
- Signal Processing top 0.2%
- Music and Audio Processing 3
- Artificial Intelligence top 0.5%
- Sentiment Analysis and Opinion Mining 6
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- Face and Expression Recognition 3
- Pharmacy top 2%
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- Color perception and design 7
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- EEG and Brain-Computer Interfaces 3
- Autism Spectrum Disorder Research 3
- Co-authors
- Shrikanth NarayananSungbok LeeChi-Chun LeeCarlos BussoAbe KazemzadehSamuel KimMurtaza BulutMaja J. Matarić
- Journals
- Speech Communication (2 papers)IEEE Transactions on Multimedia (1 paper)Language Resources and Evaluation (1 paper)
- Partner nations
- United StatesGermanyRussia
In The Last Decade
Emily Mower
21 papers receiving 3.0k citations
Hit Papers
Peers
Comparison fields: 5 of 92
- Experimental and Cognitive Psychology 2.4k
- Signal Processing 1.5k
- Artificial Intelligence 1.8k
- Computer Vision and Pattern Recognition 577
- Pharmacy 141
Countries citing papers authored by Emily Mower
This map shows the geographic impact of Emily Mower'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 Emily Mower with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Emily Mower more than expected).
Fields of papers citing papers by Emily Mower
This network shows the impact of papers produced by Emily Mower. 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 Emily Mower. The network helps show where Emily Mower may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Emily Mower, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Recognition of Physiological Data for a Motivational Agent | 2011 | 1 |
| 2 | 2011 | 23 | |
| 3 | 2011 | 48 | |
| 4 | 2011 | 11 | |
| 5 | 2011 | 243 | |
| 6 | 2010 | 157 | |
| 7 | 2010 | 38 | |
| 8 | 2010 | 7 | |
| 9 | 2010 | 3 | |
| 10 | 2010 | 1 | |
| 11 | 2009 | 48 | |
| 12 | 2009 | 90 | |
| 13 | 2009 | 6 | |
| 14 | Development of Socially Assistive Robots For Children With Autism Spectrum Disorders | 2009 | 10 |
| 15 | 2009 | 48 | |
| 16 | IEMOCAP: interactive emotional dyadic motion capture databasebreakdown → | 2008 | 2204 |
| 17 | 2008 | 9 | |
| 18 | 2008 | 6 | |
| 19 | 2007 | 219 | |
| 20 | 2007 | 32 |
About Emily Mower
Emily Mower is a scholar working on Experimental and Cognitive Psychology, Social Psychology and Cognitive Neuroscience, having authored 21 papers that have together received 3.2k indexed citations. Recurring topics across this work include Emotion and Mood Recognition (15 papers), Color perception and design (7 papers), Sentiment Analysis and Opinion Mining (6 papers), Multisensory perception and integration (4 papers), Music and Audio Processing (3 papers), EEG and Brain-Computer Interfaces (3 papers), Face and Expression Recognition (3 papers) and Autism Spectrum Disorder Research (3 papers). The work is most often cited by research in Experimental and Cognitive Psychology (2.4k citations), Signal Processing (1.5k citations) and Artificial Intelligence (1.8k citations). Emily Mower has collaborated with scholars based in United States, Germany and Russia. Frequent co-authors include Shrikanth Narayanan, Sungbok Lee, Chi-Chun Lee, Carlos Busso, Abe Kazemzadeh, Samuel Kim, Murtaza Bulut, Maja J. Matarić, Kristian Kroschel and Michael Grimm. Their work appears in journals such as Speech Communication, IEEE Transactions on Multimedia, Language Resources and Evaluation, AI Magazine and IEEE Transactions on Audio Speech and Language Processing.
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.