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Dr. Emily Reed

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. 
Amir Hossein Daraie

Amir Hossein Daraie

Amir Hossein Daraie is a Ph.D. candidate in the Biomedical Engineering Department at The Johns Hopkins School of Medicine and an M.Eng. student at the Applied Mathematics and Statistics at Johns Hopkins University. He is working on epilepsy seizure prediction, brain stimulation, and seizure network analysis under Professor Sridevi Sarma, Dr. Adam Charles and Dr. Joon Yi-Kang, MD. He earned his dual bachelor’s degree in biomedical and electrical engineering at Tehran Polytechnic. He worked as a research intern at Donders Centre for Cognitive Neuroimaging,
Netherlands. At the Donders Sleep & Memory Lab he developed soft- and hardware solutions for home-based sleep recordings and sleep modulation using single-electrode EEG and deep and machine learning algorithms under the supervision of Prof. Martin Dresler. He has several years of experience as a software developer and embedded system designer in international robotics competitions, RoboCup. 

Tony Wei

Tony Wei

Tony Wei earned his B.S. in Biomedical Engineering & B.S. in Applied Mathematics and Statistics from the Johns Hopkins University. He is currently a Ph.D. candidate in Biomedical Engineering in Dr. Sridevi Sarma’s Neuromedical Control Systems Lab at the Johns Hopkins University. His research interests include neural signal processing, machine learning applications in neuroscience, and chronic pain. For his PhD he is collaborating with Dr. Latremoliere and Dr. Alexandre from the Neurosurgery Pain Research Institute at Johns Hopkins to identify an EEG neuropathic pain biomarker from mice fronto-parietal brain activity during sleep.

Daniel Dorman

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.

Patrick Myers

Patrick Myers

Patrick Myers earned his B.S. and M.S.E. in Biomedical Engineering from the Johns Hopkins University. His recent research includes creating a network-based biomarker for epilepsy, generated from analyzing scalp EEG recordings.  He is currently a Doctor of Engineering candidate in the Neuromedical Control Systems Laboratory and Assistant Research Scientist with the Institute of Computational Medicine. For this program, he is collaborating with Dr. Yun Guan to develop a robust closed-loop stimulation system to control chronic pain. 

Sridevi V. Sarma

Sridevi V. Sarma

Dr. Sridevi Sarma received the B.S. degree in electrical engineering from Cornell University, Ithaca NY, in 1994; and an M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in, Cambridge MA, in 1997 and 2006, respectively. From 2000-2003 she took a leave of absence to start a data analytics company. From 2006–2009, she was a Postdoctoral Fellow in the Brain and Cognitive Sciences Department at the Massachusetts Institute of Technology, Cambridge. She is now an associate professor in the Institute for Computational Medicine, Department of Biomedical Engineering, at Johns Hopkins University, Baltimore MD. Her research interests include modeling, estimation and control of neural systems using electrical stimulation. She is a recipient of the GE faculty for the future scholarship, a National Science Foundation graduate research fellow, a L’Oréal For Women in Science fellow, the Burroughs Wellcome Fund Careers at the Scientific Interface Award, the Krishna Kumar New Investigator Award from the North American Neuromodulation Society, and a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) and the Whiting School of Engineering Robert B. Pond Excellence in Teaching Award.

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