Tianshi Chen

3.4k total citations · 2 hit papers
98 papers, 2.4k citations indexed

About

Tianshi Chen is a scholar working on Control and Systems Engineering, Civil and Structural Engineering and Artificial Intelligence. According to data from OpenAlex, Tianshi Chen has authored 98 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 85 papers in Control and Systems Engineering, 35 papers in Civil and Structural Engineering and 26 papers in Artificial Intelligence. Recurrent topics in Tianshi Chen's work include Control Systems and Identification (69 papers), Fault Detection and Control Systems (48 papers) and Structural Health Monitoring Techniques (35 papers). Tianshi Chen is often cited by papers focused on Control Systems and Identification (69 papers), Fault Detection and Control Systems (48 papers) and Structural Health Monitoring Techniques (35 papers). Tianshi Chen collaborates with scholars based in China, Sweden and Italy. Tianshi Chen's co-authors include Lennart Ljung, Gianluigi Pillonetto, Henrik Ohlsson, Giuseppe De Nicolao, Francesco Dinuzzo, Biqiang Mu, Alessandro Chiuso, Martin S. Andersen, Xin Yao and Jie Huang and has published in prestigious journals such as IEEE Transactions on Automatic Control, Automatica and IEEE Transactions on Signal Processing.

In The Last Decade

Tianshi Chen

93 papers receiving 2.3k citations

Hit Papers

Kernel methods in system identification, machine learning... 2012 2026 2016 2021 2014 2012 100 200 300 400

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tianshi Chen China 25 1.8k 765 614 336 193 98 2.4k
Gianluigi Pillonetto Italy 30 2.2k 1.2× 787 1.0× 860 1.4× 392 1.2× 378 2.0× 142 3.4k
Tohru Katayama Japan 19 1.8k 1.0× 378 0.5× 247 0.4× 194 0.6× 105 0.5× 147 2.2k
M. Milanese Italy 30 2.5k 1.4× 492 0.6× 343 0.6× 557 1.7× 99 0.5× 156 3.5k
Marco Lovera Italy 30 2.1k 1.2× 374 0.5× 214 0.3× 132 0.4× 219 1.1× 225 3.3k
Cristian R. Rojas Sweden 21 1.0k 0.6× 225 0.3× 196 0.3× 228 0.7× 248 1.3× 176 1.6k
Roland Tóth Netherlands 27 2.2k 1.2× 338 0.4× 192 0.3× 185 0.6× 145 0.8× 193 2.9k
Jian Pan China 22 2.0k 1.1× 548 0.7× 659 1.1× 62 0.2× 298 1.5× 55 2.3k
Vincent Verdult Netherlands 16 1.4k 0.8× 358 0.5× 177 0.3× 147 0.4× 141 0.7× 43 1.9k
J. Bokor Hungary 31 3.0k 1.7× 951 1.2× 232 0.4× 281 0.8× 147 0.8× 386 4.6k

Countries citing papers authored by Tianshi Chen

Since Specialization
Citations

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

Fields of papers citing papers by Tianshi Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tianshi Chen

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

All Works

20 of 20 papers shown
1.
Mu, Biqiang, et al.. (2025). On kernel design for regularized Volterra series identification of Wiener–Hammerstein systems. Automatica. 179. 112457–112457.
2.
Wang, Chenxiao, et al.. (2025). Parameterized gain-constrained Kalman Filtering via singular value decomposition. Automatica. 174. 112103–112103. 1 indexed citations
3.
Chen, Tianshi, et al.. (2024). A Family of Hyperparameter Estimators Linking EB and SURE for Kernel-Based Regularization Methods. IEEE Transactions on Automatic Control. 69(12). 8674–8689. 1 indexed citations
5.
Yu, Xian & Tianshi Chen. (2023). Distributed Iterative Learning Control of Nonlinear Multiagent Systems Using Controller-Based Dynamic Linearization Method. IEEE Transactions on Cybernetics. 54(8). 4489–4501. 11 indexed citations
6.
Yu, Xian, Zhongsheng Hou, & Tianshi Chen. (2023). Data-Driven Distributed Adaptive Consensus Tracking of Nonlinear Multiagent Systems: A Controller-Based Dynamic Linearization Method. IEEE Transactions on Systems Man and Cybernetics Systems. 53(11). 6953–6965. 11 indexed citations
7.
Yu, Xian, et al.. (2023). Kernel-based regularized iterative learning control of repetitive linear time-varying systems. Automatica. 154. 111047–111047. 5 indexed citations
8.
Chen, Tianshi, et al.. (2023). Towards Scalable Kernel-Based Regularized System Identification. 1498–1504. 1 indexed citations
9.
Mu, Biqiang, Tianshi Chen, Changming Cheng, & Er‐Wei Bai. (2022). Persistence of excitation for identifying switched linear systems. Automatica. 137. 110142–110142. 3 indexed citations
10.
Huang, Yunping, Can Chen, Zicheng Su, et al.. (2021). Bus arrival time prediction and reliability analysis: An experimental comparison of functional data analysis and Bayesian support vector regression. Applied Soft Computing. 111. 107663–107663. 31 indexed citations
11.
Chen, Tianshi, et al.. (2020). On Effects of Condition Number of Regression Matrix upon Hyper-parameter Estimators for Kernel-based Regularization Methods.. arXiv (Cornell University). 1 indexed citations
12.
Yin, Feng, et al.. (2020). Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series. IEEE Transactions on Signal Processing. 68. 5260–5275. 40 indexed citations
13.
Andersen, Martin S. & Tianshi Chen. (2020). Smoothing Splines and Rank Structured Matrices: Revisiting the Spline Kernel. SIAM Journal on Matrix Analysis and Applications. 41(2). 389–412. 11 indexed citations
14.
Jiang, Teng, Dabo Xu, Tianshi Chen, & Andong Sheng. (2019). Parameter estimation of discrete-time sinusoidal signals: A nonlinear control approach. Automatica. 109. 108510–108510. 7 indexed citations
15.
Chen, Tianshi, Martin S. Andersen, Alessandro Chiuso, Gianluigi Pillonetto, & Lennart Ljung. (2014). Anomaly detection in homogenous populations: A sparse multiple kernel-based regularization method. 48. 265–270. 1 indexed citations
16.
Pillonetto, Gianluigi, Francesco Dinuzzo, Tianshi Chen, Giuseppe De Nicolao, & Lennart Ljung. (2014). Kernel methods in system identification, machine learning and function estimation: A survey. Automatica. 50(3). 657–682. 494 indexed citations breakdown →
18.
Chen, Tianshi, Jun He, Guangzhong Sun, Guoliang Chen, & Xin Yao. (2009). A New Approach for Analyzing Average Time Complexity of Population-Based Evolutionary Algorithms on Unimodal Problems. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 39(5). 1092–1106. 41 indexed citations
19.
Chen, Tianshi & Jie Huang. (2008). Global robust stabilization of nonlinear strict feedforward systems with input unmodeled dynamics. 43. 3422–3427. 3 indexed citations
20.
Chen, Tianshi, Ke Tang, Guoliang Chen, & Xin Yao. (2007). On the analysis of average time complexity of estimation of distribution algorithms. University of Birmingham Research Portal (University of Birmingham). 453–460. 26 indexed citations

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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