Ting-Hao Huang

133 total papers · 1.1k total citations
56 papers, 602 citations indexed

About

Ting-Hao Huang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Science Applications. According to data from OpenAlex, Ting-Hao Huang has authored 56 papers receiving a total of 602 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 19 papers in Computer Vision and Pattern Recognition and 11 papers in Computer Science Applications. Recurrent topics in Ting-Hao Huang's work include Topic Modeling (17 papers), Multimodal Machine Learning Applications (11 papers) and Natural Language Processing Techniques (11 papers). Ting-Hao Huang is often cited by papers focused on Topic Modeling (17 papers), Multimodal Machine Learning Applications (11 papers) and Natural Language Processing Techniques (11 papers). Ting-Hao Huang collaborates with scholars based in United States, Taiwan and Israel. Ting-Hao Huang's co-authors include Jeffrey P. Bigham, Lun‐Wei Ku, Amos Azaria, Hsin‐Hsi Chen, Illah Nourbakhsh, Walter S. Lasecki, Bing‐Yu Chen, Francis Ferraro, Nasrin Mostafazadeh and Devi Parikh and has published in prestigious journals such as IEEE Transactions on Consumer Electronics, Patterns and ACM Transactions on Interactive Intelligent Systems.

In The Last Decade

Ting-Hao Huang

54 papers receiving 579 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Ting-Hao Huang 298 225 70 68 46 56 602
Meng Xia 172 0.6× 210 0.9× 93 1.3× 64 0.9× 59 1.3× 47 535
Toby Jia-Jun Li 310 1.0× 130 0.6× 52 0.7× 133 2.0× 146 3.2× 60 678
Stefano Valtolina 148 0.5× 76 0.3× 46 0.7× 83 1.2× 63 1.4× 50 557
Pengyuan Zhou 156 0.5× 109 0.5× 88 1.3× 36 0.5× 110 2.4× 42 579
Danna Gurari 219 0.7× 317 1.4× 105 1.5× 54 0.8× 21 0.5× 41 598
Angélica de Antonio 192 0.6× 108 0.5× 84 1.2× 59 0.9× 86 1.9× 85 516
Yixuan Zhang 244 0.8× 168 0.7× 22 0.3× 39 0.6× 89 1.9× 61 684
Eduardo Mosqueira-Rey 233 0.8× 57 0.3× 36 0.5× 80 1.2× 115 2.5× 31 638
J.D. Zamfirescu-Pereira 255 0.9× 55 0.2× 70 1.0× 82 1.2× 88 1.9× 21 526
Malcolm Ryan 192 0.6× 212 0.9× 30 0.4× 21 0.3× 35 0.8× 48 638

Countries citing papers authored by Ting-Hao Huang

Since Specialization
Citations

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

Fields of papers citing papers by Ting-Hao Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ting-Hao Huang. 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 Ting-Hao Huang. The network helps show where Ting-Hao Huang may publish in the future.

Co-authorship network of co-authors of Ting-Hao Huang

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

All Works

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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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