• Epilepsy is a chronic neurological disease that affects nearly 60 million people worldwide and is defined by recurrent and unprovoked seizures. If the patient’s seizures are not controlled with anti-epileptic medications, they often lose their independence, and can experience profound behavioral, psychological, cognitive, social, and financial burdens. Currently, diagnosis and treatment of epilepsy involves visual inspection, channel by channel, of electroencephalographic (EEG) signals recorded noninvasively from the scalp or invasively through intracranial monitoring. These methods of EEG assessment are extremely time-consuming, subjective, and disregard the network phenomena that drive seizure genesis. My research has focused on the development of novel computational tools that investigate epilepsy with a network framework to characterize the disease more objectively and accurately. Incorporation of such computational tools into clinical practice will advance current clinical decision support systems, and has the potential to significantly improve treatment outcomes.
  • Research Overview

  • dynamical network models; signal processing; epilepsy; computational modeling; neural stimulation; neuroengineering; neuromodulation
  • Education And Training

  • Doctor of Philosophy in Biomedical / Medical Engineering, University of California 2019
  • Master of Engineering in Biomedical / Medical Engineering, University of California 2017
  • Bachelor of Engineering in Biomedical / Medical Engineering, University of Tennessee System : Knoxville 2014
  • Full Name

  • Rachel Smith