Jeff Craley

Dr. Jeff Craley earned his PhD in Electrical and Computer Engineering from Johns Hopkins University in 2022. His dissertation research focused on developing machine learning algorithms for seizure localization in scalp EEG recordings. He earned an MS in Electrical Engineering from Boston University in 2015. He also earned a BS in Aerospace Engineering and BA in English Literature from Virginia Tech in 2011.

Emily Reed

Dr. Emily A. Reed earned a PhD in Electrical and Computer Engineering at the University of Southern California (USC). In 2019 she obtained an MSc in Electrical Engineering from the University of Southern California and in 2017 she earned a BS degree in Electrical and Computer Engineering with honors research and global engineering distinction from the Ohio State University.
Dr. Reed is interested in designing and analyzing novel control strategies, algorithms, and machine learning tools to understand better, predict, and control complex dynamical networks.
Dr. Reed has been awarded 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. In 2022 Dr. Reed was named a 2023 Rising Star in Electrical Engineering and Computer Science in the USA. Dr. Reed is also a Ming Hsieh Scholar at USC, a distinction awarded to the top 6 PhD students in the ECE department. 

Daniel Dorman

Daniel Dorman earned his B.S. in biomedical engineering from LeTourneau University in Longview, Texas, and his PhD in neuroscience from George Mason University. His doctoral dissertation focused on developing biophysically detailed computational models of individual neurons of the striatum to investigate mechanisms underlying synaptic plasticity, learning, and memory. He joined the Neuromedical Control Systems Lab as a postdoc in 2021 as an IRACDA postdoctoral fellow in Johns Hopkins University’s ASPIRE program. In the NCSL his research focuses on models of decision making and analysis of EEG data collected during decision making experiments to identify brain networks that support decision making and executive function.