Emily Reed

Emily Reed

(she/her/hers)

University of Southern California

cyber-physical systems, control theory, complex networks, cyber-neural systems

Emily A. Reed is currently an Electrical and Computer Engineering Ph.D. student at the University of Southern California. In 2017, she received her B.S. degree in Electrical and Computer Engineering with honors research and global engineering distinction from The Ohio State University and in 2019 her M.S. in Electrical Engineering from the University of Southern.
Emily is interested in designing and analyzing novel control strategies, algorithms, and machine learning tools to better understand, predict, and control complex dynamical networks. Emily has received several fellowships including the National Science Foundation Graduate Research Fellowship, the National Defense Science and Engineering Graduate Fellowship, the USC Annenberg merit fellowship, one of USC's most prestigious fellowships, and the Qualcomm USC Women in Science and Engineering merit fellowship. Emily was nominated as a Best Student Paper Finalist at the 42nd Annual International Virtual Conferences of the IEEE Engineering in Medicine and Biology Society in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering in July 2020.

Fractal Dynamical Network Modeling of Brain Activity and its Applications to Epilepsy: A Paradigm Shift

Epilepsy affects approximately 50 million people worldwide. Despite its prevalence, the recurrence of seizures can be mitigated only 70% of the time through medication. Furthermore, surgery success rates range from 30% - 70% because of our limited understanding of how and where a seizure starts. Fortunately, for these patients, other treatments, such as neurostimulation, exist. My research focuses on designing effective mitigation strategies, such as neurostimulation techniques that rely on the use of fractional-order dynamical networks. Remarkably, fractional-order dynamical systems have been shown to accurately model neural behavior. By examining this model and deriving its important properties, I have developed novel methods to mitigate seizures in the brain and have uncovered new insights into novel treatments of epilepsy.