Ph.D. The Chinese University of Hong Kong

M.E. Harbin Institute of Technology

B.E. Harbin Institute of Technology

Tianshi Chen received his Ph.D. in Automation and Computer-Aided Engineering from The Chinese University of Hong Kong in 2008/12. From 2009/04 to 2015/12, he was working in the Division of Automatic Control, Department of Electrical Engineering, Linköping University, Linköping, Sweden, first as a Postdoc and then (from April 2011) as an Assistant Professor. He is now a Professor at the Chinese University of Hong Kong, Shenzhen.

His research interests lie primarily in the areas of AI methods for system identification and data-driven control, statistical modelling of spatial-temporal big data, high precision control, and asymptotic theory for system identification, and in particular

• AI methods for system identification and data-driven control: Deep learning methods, Bayesian inference methods, Kernel methods, and Regularization methods

• Statistical modelling of spatial-temporal big data: Gaussian process regression, Bayesian manifold regularization, Physics-informed deep learning methods

• High precision control: Robot control, Process control, Motion control

• Asymptotic theory for system identification: Large sample theory, Large dimension theory

He has published over 100 peer-reviewed papers including 35 papers in IFAC Automatica and IEEE Transactions on Automatic Control, including 1 survey paper and 18 regular/full papers. He has participated in research projects in Sweden, Europe and China. As a principal investigator, the total amount of research grants he has received is 10.0 M CNY + 3.6M SEK.

He is/was an associate editor for

• IFAC Automatica (2017/01-present), 

• IEEE Transactions on Automatic Control (2023/10-2026/12), 

• System & Control Letters (2017/01-2020/12), 

• IEEE CSS Conference Editorial Board (2016/07-2019/08).

He received several research and teaching awards, including 

• the Oversea High-Level Youth Talents Award of China, 2015,

• the Presidential Research Fellow Award of CUHK-SZ, 2020, 

• the 2021 World’s Top 2 Scientists, 2022,

• the Presidential Exemplary Teaching Award of CUHK-SZ, 2021, 

• the First-Class Undergraduate Course of GuangDong Province, 2022,

• the Outstanding Teacher Award of Shenzhen, 2022.

He was one of four plenary speakers at the 19th IFAC Symposium on System Identification, Padova, Italy, 2021, and he is a coauthor of the book “Regularized System Identification - Learning Dynamic Models from Data”.

[1] Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung, “Regularized System Identification – Learning Dynamic Models from Data”, Springer, 2022. (open access: https://link.springer.com/book/10.1007/978-3-030-95860-2)

[2] Biqiang Mu, and Tianshi Chen “On Asymptotic Optimality of Cross-Validation based Hyper-parameter Estimators for Kernel-based Regularized System Identification”, IEEE Transactions on Automatic Control, vol. 69(7), pp. 4352 – 4367, 2024.

[3] Xiaozhu Fang and Tianshi Chen, “On Kernel Design for Regularized Non-Causal System Identification”, Automatica, vol. 159, pp. 111335, 2024.

[4] Biqiang Mu, Lennart Ljung and Tianshi Chen, “When cannot regularization be used to improve the least squares estimate in the regularized system identification”, Automatica, vol. 160, pp. 111442, 2024

[5] Yue Ju, Biqiang Mu, Lennart Ljung, and Tianshi Chen, “Asymptotic Theory for Regularized System Identification Part I: Empirical Bayes Hyper-parameter Estimator”, IEEE Transactions on Automatic Control, vol. 68, pp. 7224 – 7239, 2023.

[6] Xian Yu, Xiaozhu Fang, Biqiang Mu, and Tianshi Chen, “Kernel-based Regularized Iterative Learning Control of Repetitive Linear Time-varying Systems”, Automatica, vol. 154, pp. 111047, 2023.

[7] Junpeng Zhang, Yue Ju, Biqiang Mu, Renxin Zhong, and Tianshi Chen, “An Efficient Implementation for Spatial-Temporal Gaussian Process Regression and Its Applications”, Automatica, vol. 147, pp. 110679, 2022. 

[8] Tianshi Chen and Martin S. Andersen, “On Semiseparable Kernels and Efficient Implementation of Regularized System Identification and Function Estimation”, Automatica, vol. 132, pp. 109682, 2021. 

[9] Martin Lindfors, and Tianshi Chen. “Regularized System Identification in the Presence of Outliers: a Variational EM Approach”, Automatica, vol. 121, pp. 109152, 2020.

[10] Lennart Ljung, Tianshi Chen, and Biqiang Mu. “A Shift in Paradigm for System Identification”, International Journal of Control, vol. 93(2), pp. 173–180, 2020. 

[11] Martin S. Andersen and Tianshi Chen, “Smoothing Splines and Rank Structured Matrices: Revisiting the Spline Kernel”, SIAM Journal on Matrix Analysis and Applications, vol. 41, pp. 389–412, 2020.

[12] Tianshi Chen. “Continuous-time DC kernel — a stable generalized first-order spline kernel,” IEEE Transactions on Automatic Control, vol. 63. no. 12, pp. 4442–4447, 2018. 

[13] Biqiang Mu, and Tianshi Chen. “On input design for regularized LTI system identification: Power-constrained inputs,” Automatica, vol. 97, pp. 327–338, 2018. 

[14] Tianshi Chen and Gianluigi Pillonetto. “On the stability of reproducing kernel Hilbert spaces of discrete-time impulse responses,” Automatica, vol. 95, pp. 529–533, 2018. 

[15] Biqiang Mu, Tianshi Chen and Lennart Ljung. “On Asymptotic Properties of Hyperparameter Estimators for Kernel-based Regularization Methods,” Automatica, vol. 94, pp. 381–395, 2018.

[16] Tianshi Chen. “On kernel design for regularized LTI system identification,” Automatica, vol. 90, pp. 109-122, 2018. 

[17] John Lataire and Tianshi Chen. “Transfer function and transient estimation by Gaussian process regression in the frequency domain,” Automatica, Vol. 72. pp. 217–229, 2016. 

[18] Tianshi Chen, Martin Andersen, Lennart Ljung, Alessandro Chiuso, and Gianluigi Pillonetto, “System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques,” IEEE Transactions on Automatic Control, Vol.59, No. 11, pp. 2933–2945, 2014. 

[19] Gianluigi Pillonetto, Francesco Dinuzzo, Tianshi Chen, Giuseppe De Nicolao, and Lennart Ljung, “Kernel Methods in System Identification, Machine Learning and Function Estimation: A Survey,” Automatica, Vol.50, No. 3, pp.657–682, 2014. 

[20] Tianshi Chen, and Lennart Ljung, “Implementation of algorithms for tuning parameters in regularized least squares problems in system identification,” Automatica, no.8 pp. 1525–1535, 2013. 

[21] Tianshi Chen, Henrik Ohlsson, and Lennart Ljung, “On the estimation of transfer functions, Regularizations and Gaussian processes - revisited,” Automatica, no. 8, pp. 1525–1535, 2012.