• 580.691/491 Learning, Estimation, and Control
    The course introduces modern techniques in mathematical analysis of biomedical data. Techniques include maximum likelihood, estimation theory via Kalman equation, state-space models, Bayesian estimation, classification of labeled data, support vector machine, dimensionality reduction via principal component analysis, clustering, expectation maximization, and dynamic programming via the Bellman equation.


  • 580.423/623 Systems Bioengineering: The nervous systems
    This is one of the core courses in undergraduate education at Hopkins BME. The purpose of this course is to introduce the central nervous system from an engineering perspective.This course is taught in the spring semester.

  • Short course on computational motor control
    This is a short course that provides an overview for the mathematics that has been used to formulate problems in motor control.

  • 440.600 Core Course on Neuroscience   
    This course introduces the human central nervous system to first year medical students and graduate students at Johns Hopkins.  The four lectures introduce the spinal motor structures, descending tracts, posterior parietal cortex, and the motor system of the frontal lobe.  Last taught in 2013.

  • 580.431/631 Computational Motor Control
    This course uses topics from robotics, control theory, and neuroscience to understand in some depth the primate motor system. Our approach is to use mathematics to explore functions of muscles, spinal reflex systems, posterior parietal cortex, frontal motor areas, cerebellum, and basal ganglia. Our focus is on how these various parts of the motor system contribute to the control and learning of reaching movements. Last taught in 2003.