Kai Kang

600 citations
12 papers · 328 indexed · h-index 7
Topics
Target Tracking and Data Fusion in Sensor Networks (5 papers)Single-cell and spatial transcriptomics (2 papers)Microwave Imaging and Scattering Analysis (2 papers)

In The Last Decade

Kai Kang

12 papers receiving 319 citations

Peers

Kai Kang
Comparison fields: 5 of 76
  • Molecular Biology 202
  • Cancer Research 100
  • Artificial Intelligence 54
  • Pulmonary and Respiratory Medicine 33
  • Oncology 25
Replace Xiongjun Ye with:
Xiongjun Ye China
Nishanth Ulhas Nair United States
Ke Qi China
Jiahong Yang China
Cheng‐Ran Xu China
Kuan-Song Wang China
Xiaolong Cheng China
Matthew Poole United Kingdom
Krzysztof Fujarewicz Poland
Fei Sun China
Kai Kang relative to Xiongjun Ye China Xiongjun Ye's profile →
Citations per field
00.5×1.7×
Xiongjun Ye · 1×
Citations per year

Countries citing papers authored by Kai Kang

Since Specialization
Citations

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

Fields of papers citing papers by Kai Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kai Kang

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

All Works

12 of 12 papers shown
#WorkIndexed citations
1 10
2 78
3 5
4 43
5 134
6 19
7
A multilevel homotopy MCMC sequential Monte Carlo filter for multi-target tracking
1
8
A learning drift homotopy particle filter
5
9
Drift homotopy particle filter for non-Gaussian multi-target tracking
9
10
Drift homotopy methods for a non-Gaussian filter
5
11 16
12 3

About Kai Kang

Kai Kang is a scholar working on Cancer Research, Artificial Intelligence and Biochemistry, having authored 12 papers that have together received 328 indexed citations. Recurring topics across this work include Target Tracking and Data Fusion in Sensor Networks (5 papers), Single-cell and spatial transcriptomics (2 papers) and Microwave Imaging and Scattering Analysis (2 papers). The work is most often cited by research in Cancer Research (100 citations), Molecular Biology (202 citations) and Biochemistry (13 citations). Kai Kang has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Leping Li, David M. Umbach, Kevin Lee, Yuanyuan Li, J.M. Krahn, Vasileios Maroulas, Igor Shats, Xiaoling Li, Ioannis D. Schizas and Jason G. Williams. Their work appears in journals such as Nature Communications, BMC Bioinformatics and PLoS Computational Biology.

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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