Research Overview Videos


My research interests are twofold: (i) fundamental research in automatic control/control systems, and (ii) cyber-neural systems.

The first 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 dynamical and control systems, 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.  Among the different objectives, we seek to ally performance with privacy and security/resilience guarantees.

Secondly, my core research is on neural systems towards making cyber-neural systems a reality. These implement the control for neural systems that require embedding pervasive intelligence toward personalized therapeutics. Toward this goal, we focus on developing digital twins that serve as a platform to devise discharging strategies for electrical neurostimulation devices capable of mitigating abnormal behavior (e.g., epileptic seizure).

Fundamental Research in the Analysis, Design, and Optimization of Large-Scale Networked Dynamical Systems

(Automatic Control/Control Systems)

Our capability to understand and interact with a system depends on being able to answer some fundamental questions. For instance,

Additionally, the above questions can be addressed under privacy requirements and/or adversarial environments (i.e., security), thus leading to the following questions:

Check out our recent survey on Automatica

Functional Magnetic Resonance Imaging (Neuroscience and Medicine)

Understanding the relationship between structural brain connectivity and functional connectivity dictated by blood-oxygenation dependency level (BOLD) across different brain regions is key to understanding the brain in health and disease. The latter may exhibit spatiotemporal fingerprints that are uniquely associated with specific stages of disease that can play a key role in digital pathology and assessing the performance of new therapeutics to mitigate their effects.

Re-thinking electroencephalographic (EEG) wearables

(Neuroscience and Medicine)

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'. Thus, improving the reliability of the interaction between brain-machine and brain-machine-brain interfaces, with potential applications to neurological disorders (e.g. epilepsy). 

Personalized Neurostimulation


Today's neurostimulation devices have discharging strategies that are determined empirically. Therefore, these devices do not take into consideration the specific brain's dynamics or response to a sequence of discharges. Hence, we seek model-based schemes that enable a personalized discharge scheme that minimizes the interaction with the brain, which leads to better performance and an increase in autonomy for the neurostimulation device.