Research Interests

He has been mainly working in the area of system identification, statistical signal processing, machine learning, nonlinear control and their applications. His current research interests include system identification with machine learning methods, state inference by exploring the structure of systems, and control of nonlinear feedforward systems and their applications.

System identification with machine learning methods

Regularization methods

Computational methods, such as SMC, MCMC, etc.

Sparsity methods and its applications in structure detection, e.g., dynamic network structure detection, anomaly detection, segmentation, hybrid system identification.

State inference by exploring the structure of systems

Decentralized and parallel state inference algorithms and their implementation

State inference with equality constraints

Nonlinear control theory and applications

Robust stabilization and output regulation of nonlinear systems

Small gain theorems (Input-to-state stability)

Control of feedforward systems

Adaptive input disturbance suppression