Our lab seeks to understand neuronal patterns in the central nervous system in both health and in disease. We apply a variety of computational techniques to model and modulate neuronal behaviors including mechanistic modeling, statistical modeling, dynamical systems and control techniques. We often work with multivariate time series data (e.g. EEG) and apply system identification and machine learning techniques to uncover patterns in neuronal data. Our applications include epilepsy, chronic pain, and to some extent, brain-machine interfaces. We collaborate closely with electrophysiologists and clinicians with the ultimate goal of designing better diagnostics and treatments for diseases of the central nervous system.