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 machine learning techniques to uncover patterns in neuronal data. Our applications span Parkinson's disease, epilepsy, brain-machine interfaces, chronic pain, decision making, and sleep. We collaborate closely with electrophysiologists and clinicians with the ultimate goal of designing better treatment for diseases of the central nervous system.