The research focuses on quantum systems, their control and the emerging applications in information technologies. In particular, we are interested in probabilistic models for quantum noise and quantum feedback, generation of entangled states (i.e. states that exhibit multi-system quantum correlations, not reproducible using classical variables) on networks of systems, and quantum walks on graphs. Key aspects include assessing scalabiltiy, speed and robustness of the control strategies. Ongoing research projects aim to study:   

Spectral estimation is the science of building models in the frequency domain from measured data. Our research focuses on the development of spectral estimation techniques building models with high resolution in prescribed frequency bands.

Computer vision is the science of retrieving information from images and movies (sequences of images indexed by time). Our goal is to develop methodologies to enable artificial systems to use visual sensors (such as cameras) to interact with the environment similarly to what us humans do.

Machine Learning is about endowing artificial systems the ability to learn from experience. Our research in this field spans a broad range of topics and includes building models from measured data as well as learning actions from "experience".

The research focuses on the analysis of the main theoretical properties of this class of systems, both in the continuous and in the discrete time cases, with possible applications in the context of drug treatment modeling, fluid systems and thermal models. Ongoing projects are the following:

Broadly speaking, a fundamental problem in control theory is to automatically generate the control input that acts on the behaviour of such system in the best possible way. This is done by minimizing a cost criterion or performance index which penalises i) the deviations of key measurements from their desired values and ii) the control effort, because energy has its price: this is in particular the goal of optimal control.