 My research is motivated by the dearth of scalable techniques for the analysis and synthesis of various large-scale complex systems; notably, the ones which tackle design and decision making in a single framework. Utilizing concepts from control, graph theory and combinatorial optimization, the main objective is to develop new design tools and algorithms that can harness the physical dynamics of such systems to meet the specified large-scale control objectives with performance guarantees. The systems of interest are neural-systems, and distributed control systems that require the embedding of pervasive intelligence and scalable decision-making; a particularly relevant infrastructure being the electrical power network as it transitions to the smart grid of the future, and multi-agent networks.
Neurosciences and Control My motivation is drawn from the following questions:
Closed-loop Electroencephalography-based (EEG) Neurostimulation Devices  Mapping Structural into Functional Connectivity  Re-Thinking Wearable Technology  State-of-the-art electroencephalogram (EEG) wearable technology relies on signal processing and machine learning tools to capture mainly temporal features that can be linked to a specific task. Our approach aims to introduce new models capable of capturing spatiotemporal dependencies, to be later used to determine which regions need to be sensed, and which data contains more 'information'. Subsequently, we can leverage these insights to re-think wearable technology, by possible minimizing the number of sensors prescribed to monitor brain dynamics, which implies a smaller energy requirement and, hence, more autonomy. Furthermore, due to the spatiotemporal 'fingerprints' captured by our model, we are able to enhance the functionality of current technology. Thus, improving the reliability of the interaction between brain-machine and brain-machine-brain interfaces, with potential applications to neurological disorders (e.g. epilepsy) by improving the current neurostimulators. Alternatively, the improvement of this technology enables a real-time embedding into the current technology world. Therefore, this technology will ultimately enable the improvement of people's life quality. Analysis, Design, and Optimization of Large-Scale Networked Dynamical Systems 
Additionally, the above questions can be addressed under privacy requirements and/or adversarial environments, so I am also interested in addressing these while considering the following:
Actuation-Sensing Selection  A major focus area of my research involves the optimization and assessment of intrinsic elementary system theoretic constructs, such as controllability and observability, from a design point of view, and, in particular, in non-classical information and operation scenarios. For instance, we are interested in understanding and characterizing the sparsest inputs (actuators) and sparsest outputs (sensors) to ensure controllability and observability, when physical parameters are not accurately known. In other words, we want to assess structural controllability and observability that ensures that almost all realizations of the plant matrices with a given structure (sparsity) are controllable and observable, respectively. Actuation-Sensing-Communication Co-Design  Due to geographic nature of distributed systems or design constraints, given a system that is controllable and observable, it is desirable to only provide a relatively small subset of the measurements to some actuators. Thus, we ask the following fundamental question: Which subset of measured state variables needs to be provided to the actuators (and which actuators) to ensure arbitrary pole placement using decentralized control? It is well known that in static output feedback, if all the measurements are forwarded to all the actuators, arbitrary performance (i.e., pole placement) can be enforced as long as the system is controllable and observable. Nevertheless, if a single measurement is disregarded by an actuator, then there is a demand for theoretical and numerical methods (suitable for large-scale systems) to assert that decentralized control for arbitrary performance is still possible. Nevertheless, it is known that such performance is closely related with the notion of fixed modes, which are the modes of the closed-loop system that use static output feedback and are kept unchanged by varying the gain that satisfies a pre-specified information pattern. Thus, we seek to determine and design information patterns in large-scale dynamic systems that have no structural fixed modes, i.e., fixed modes originated by the structure of the system plant matrices. <See all publications on this topic> |
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