Kai Sheng Tai

836 citations
5 papers · 123 indexed · h-index 4
Topics
Machine Learning and Data Classification (3 papers)Earthquake Detection and Analysis (1 paper)Tensor decomposition and applications (1 paper)
Journals
Proceedings of the VLDB EndowmentJournal of Geophysical Research Solid EarthInternational Conference on Machine Learning
Partner nations
United States

In The Last Decade

Kai Sheng Tai

5 papers receiving 121 citations

Peers

Kai Sheng Tai
Comparison fields: 5 of 30
  • Artificial Intelligence 94
  • Geophysics 51
  • Computer Networks and Communications 29
  • Signal Processing 26
  • Ocean Engineering 10
Replace Robert Fung with:
Robert Fung United States
Jens-Peter Redlich Germany
Miquel Pericàs Sweden
Sian Jin United States
Jianming Li China
Ziqiang Zhang China
Sanjoy Sarawgi United States
Yuqi Huang China
Pierre Weis France
Azim Ahmadzadeh United States
Kai Sheng Tai relative to Robert Fung United States Robert Fung's profile →
Citations per field
00.5×9.7×
Robert Fung · 1×
Citations per year

Countries citing papers authored by Kai Sheng Tai

Since Specialization
Citations

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

Fields of papers citing papers by Kai Sheng Tai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kai Sheng Tai. 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 Sheng Tai. The network helps show where Kai Sheng Tai may publish in the future.

Co-authorship network of co-authors of Kai Sheng Tai

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

All Works

5 of 5 papers shown
#WorkIndexed citations
1 54
2
Equivariant Transformer Networks.
8
3 31
4 29
5
There and Back Again: A General Approach to Learning Sparse Models.
1

About Kai Sheng Tai

Kai Sheng Tai is a scholar working on Computational Mathematics, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 5 papers that have together received 123 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (3 papers), Earthquake Detection and Analysis (1 paper) and Tensor decomposition and applications (1 paper). The work is most often cited by research in Geophysics (51 citations), Artificial Intelligence (94 citations) and Signal Processing (26 citations). Kai Sheng Tai has collaborated with scholars based in United States. Frequent co-authors include Peter Bailis, Gregory C. Beroza, Weiqiang Zhu, S. Mostafa Mousavi, Gregory Valiant, Jialin Ding and Edward Gan. Their work appears in journals such as Proceedings of the VLDB Endowment, Journal of Geophysical Research Solid Earth and International Conference on Machine Learning.

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026