Miriam Cha

10 papers and 132 indexed citations i.

About

Miriam Cha is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Miriam Cha has authored 10 papers receiving a total of 132 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 2 papers in Electrical and Electronic Engineering and 2 papers in Artificial Intelligence. Recurrent topics in Miriam Cha’s work include Multimodal Machine Learning Applications (2 papers), Speech and Audio Processing (2 papers) and Sparse and Compressive Sensing Techniques (2 papers). Miriam Cha is often cited by papers focused on Multimodal Machine Learning Applications (2 papers), Speech and Audio Processing (2 papers) and Sparse and Compressive Sensing Techniques (2 papers). Miriam Cha collaborates with scholars based in United States and United Kingdom. Miriam Cha's co-authors include Youngjune Gwon, H. T. Kung, Patrick J. Wolfe, Christ D. Richmond, H. T. Kung, Ava K. Mokhtari, Theodoros Tsiligkaridis, Jonathan Parks, April E. Mendoza and Haytham M.A. Kaafarani and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, Lecture notes in computer science and Journal of Trauma and Acute Care Surgery.

In The Last Decade

Co-authorship network of co-authors of Miriam Cha i

Fields of papers citing papers by Miriam Cha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Miriam Cha

Since Specialization
Citations

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

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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