CHEN, Tianshi

Associate Professor

Education Background

PhD (The Chinese University of Hong Kong)

ME, BE (Harbin Institute of Technology)

Research Field
System Identification; Machine Learning; State Inference; Data Science; Sensor Fusion; Scalable Algorithms; Nonlinear Control

Tianshi Chen got his Bachelor’s degree with highest honor and Master’s degree both from Harbin Institute of Technology in 2001 and 2005, respectively. He got his Ph.D. degree in Automation and Computer-Aided Engineering from The Chinese University of Hong Kong in December 2008. From April 2009 to December 2015, he was working in the Division of Automatic Control, Department of Electrical Engineering, Linköping University, Linköping, Sweden, first as a Postdoc (April 2009 - March 2011) and then as an Assistant Professor (April 2011 – December 2015). In May 2015, he got the Youth Talents Award of the Thousand Talents Plan, and in December 2015, he returned to China and joined the Chinese University of Hong Kong, Shenzhen, as an Associate Professor.

He has been mainly working in the area of system identification (data-driven modeling and analysis), statistical signal processing, machine learning, data science, nonlinear control, and their applications. He has participated in several projects in Hong Kong, Sweden, Europe and China. He is the principal investigator of a general project (2018-2021) funded by the National Natural and Science Foundation of China, an advanced research grant (2017-2019) funded by Shenzhen Science and Technology Innovation Council, and a research grant (2015-2018, the success rate was 9.8%) for junior scientists funded by Swedish Research Council (VR). As a principal investigator, the total amount of competitive grants he has received so far is 7.26M CNY + 3.6M SEK. He was also a major co-worker for the advanced grant LEARN (Limitations, Estimation, Adaptivity, Reinforcement, Networks in System Identification, 2011-2015) funded by European Research Council.

He is an Associate Editor for Automatica (2017-present), System & Control Letters (2017-present), and IEEE Control System Society Conference Editorial Board (2016-present).

Academic Publications

[1] Tianshi Chen* and Martin S. Andersen. “On Semiseparable Kernels and Efficient Implementation

of Regularized System Identification and Function Estimation”, Automatica, submitted on December 16, 2019, under review.

[2] Martin Lindfors, and Tianshi Chen*. “Regularized System Identification in the Presence

of Outliers: a Variational EM Approach”, Automatica, in press, 2020.  

[3] Teng Jiang, Dabo Xu, Tianshi Chen and Andong Sheng. “Parameter Estimation of Discrete-

Time Sinusoidal Signals: A Nonlinear Control Approach”, Automatica, vol. 108, 2020.  

[4] 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.

[5] 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(2),

pp. 389–412, 2020.

[6] Yuxin Zhao, Carsten Fritsche, Gustaf Hendeby, Feng Yin, Tianshi Chen and Fredrik

Gunnarsson. “Cramér-Rao Bounds for Filtering Based on Gaussian Process State-Space

Models”, IEEE Transactions on Signal Processing, vol. 67(23), pp. 5936–5951, 2019.

[7] 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.

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

[9] 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.

[10] 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.

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

[12] Francesca P. Carli, Tianshi Chen*, and Lennart Ljung. “Maximum entropy kernels for system identification,” IEEE Transactions on Automatic Control, Vol. 62. pp. 1471–1477, 2017. 

[13] 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.

[14] Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. “Regularized linear system identification using atomic, nuclear and kernel-based norms: the role of the stability constraint,” Automatica, Vol. 69. pp. 137–149, 2016.

[15] Tianshi Chen*, Tohid Ardeshiri, Francesca P. Carli, Alessandro Chiuso, Lennart Ljung, and Gianluigi Pillonetto. “Maximum entropy properties of discrete-time first-order stable spline kernel,” Automatica, Vol. 66. pp. 34–38, 2016.

[16] 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. (regular).

[17] Henrik Ohlsson, Tianshi Chen*, Sina Khoshfetrat Pakazad, Lennart Ljung, and Shankar Sastry, “Scalable Anomaly Detection in Large Homogenous Populations,” Automatica, Vol.50, No. 5, pp. 1459–1465, 2014.

[18] 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.

[19] 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.

[20] 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.

[21] Tianshi Chen*, Thomas B. Schön, Henrik Ohlsson, and Lennart Ljung, “Decentralized particle filter with arbitrary state decomposition,” IEEE Transactions on Signal Processing, vol. 59, no. 2, pp. 465–478, 2011.

[22] Tianshi Chen*, “Comments on “state estimation for linear systems with state equality constraints” [Automatica 43 (2007) 1363-1368],” Automatica, vol. 46, pp. 1929–1932, 2010.

[23] Tianshi Chen and Jie Huang, “A small gain approach to global stabilization of nonlinear feedforward systems with input unmodeled dynamics,” Automatica, vol. 46, pp. 1028–1034, 2010.

[24] Tianshi Chen and Jie Huang, “Global robust output regulation by state feedback for strict feedforward

systems,” IEEE Transactions on Automatic Control, vol. 54, no. 9, pp. 2157–2163, 2009.

[25] Tianshi Chen and Jie Huang, “Disturbance attenuation of feedforward systems with dynamic

uncertainty,” IEEE Transactions on Automatic Control, vol. 53, no. 7, pp. 1711–1717, 2008.