The overarching theme of our group is the analysis of stochastic dynamical systems which are ubiquitous in process systems engineering. In this broad area, we study system identification, nonlinear estimation, control, density functional theory and inverse problems. The focus is on developing novel algorithms using tools derived from advanced mathematics and providing guarantees where applicable.

Research Areas

1. Inverse Algorithms in ERT: The main drawback of electrical resistance tomography is the very low spatial resolution. Our group develops/implements novel reconstruction algorithms deriving from sparse optimization and methods incorporating prior information.

2. System Identification: In traditional system identification, the order and the parameters of the model are not found together. In our group, we devise new algorithms that can estimate the parameters and order jointly. Our group incorporates studies the problem tools such as Laplace transform and sparse optimization.

3. Nonlinear estimation: Most control designs are based on information of states. However, states are not measure in many applications. Our group uses the particle filters for nonlinear state estimation and study their convergence properties.

4. Control of Fluid Systems: Control of fluid systems involves the challenge of partial differential equation models. In our group, we simulate multiphase flow processes using computational fluid dynamics to compute dynamic models for control design