Kim Batselier
- Computational Mathematics top 0.5%
- Computational Mechanics top 5%
- Computational Theory and Mathematics top 5%
- Artificial Intelligence
- Statistical and Nonlinear Physics top 10%
- Co-authors
- Ngai WongPhilippe DreesenBart De MoorWenjian YuLuca DanielJianchun LiCong ChenJohan A. K. Suykens
- Topics
- Tensor decomposition and applications (31 papers)Model Reduction and Neural Networks (16 papers)Advanced Adaptive Filtering Techniques (13 papers)
- Partner nations
- Hong KongNetherlandsBelgium
In The Last Decade
Kim Batselier
48 papers receiving 444 citations
Peers
Comparison fields: 5 of 62
- Computational Mathematics 212
- Computational Mechanics 153
- Computational Theory and Mathematics 96
- Artificial Intelligence 87
- Statistical and Nonlinear Physics 77
Countries citing papers authored by Kim Batselier
This map shows the geographic impact of Kim Batselier'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 Kim Batselier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kim Batselier more than expected).
Fields of papers citing papers by Kim Batselier
This network shows the impact of papers produced by Kim Batselier. 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 Kim Batselier. The network helps show where Kim Batselier may publish in the future.
Co-authorship network of co-authors of Kim Batselier
This figure shows the co-authorship network connecting the top 25 collaborators of Kim Batselier. A scholar is included among the top collaborators of Kim Batselier 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 Kim Batselier. Kim Batselier is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 12 | |
| 4 | 2 | |
| 5 | 13 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 27 | |
| 9 | Parallelized Tensor Train Learning For Polynomial Pattern Classification | 1 |
| 10 | 9 | |
| 11 | 3 | |
| 12 | 2 | |
| 13 | 11 | |
| 14 | A fast iterative orthogonalization scheme for the Macaulay matrix | 2 |
| 15 | 9 | |
| 16 | 3 | |
| 17 | 5 | |
| 18 | Weighted/structured total least squares problems and polynomial system solving | 0 |
| 19 | Joint Regression and Linear Combination of Time Series for Optimal Prediction | 0 |
| 20 | 8 |
About Kim Batselier
Kim Batselier is a scholar working on Computational Mathematics, Statistical and Nonlinear Physics and Computational Theory and Mathematics, having authored 51 papers that have together received 457 indexed citations. Recurring topics across this work include Tensor decomposition and applications (31 papers), Model Reduction and Neural Networks (16 papers) and Advanced Adaptive Filtering Techniques (13 papers). The work is most often cited by research in Computational Mathematics (212 citations), Computational Mechanics (153 citations) and Numerical Analysis (36 citations). Kim Batselier has collaborated with scholars based in Hong Kong, Netherlands and Belgium. Frequent co-authors include Ngai Wong, Philippe Dreesen, Bart De Moor, Wenjian Yu, Luca Daniel, Jianchun Li, Bart De Moor, Cong Chen, Johan A. K. Suykens and Zheng Zhang. Their work appears in journals such as Automatica, IEEE Transactions on Image Processing and Pattern Recognition.
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.