• 580.704 Mathematical Foundations of BME
    The course introduces modern techniques in mathematical analysis of biomedical data. Techniques include maximum likelihood, Fourier analysis, 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.

  • 580.691  Learning Theory 
    This course introduces the probabilistic foundations of learning theory. We will discuss topics in regression, estimation, optimal control, system identification, Bayesian learning, and classification. Our aim is to first derive some of the important mathematical results in learning theory, and then apply the framework to problems in biology, particularly animal learning and control of action.  However, this is not a machine learning course. Rather, our aim is to use these mathematical results to better understand learning and control processes in the central nervous system. Last taught in 2017.

  • 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.