Emily A. Reed
I am an Assistant Professor in Electrical and Computer Engineering at Texas Tech University. I graduated with my Ph.D. in Electrical and Computer Engineering from the Ming Hsieh Electrical and Computer Engineering Department at the University of Southern California in 2023, where I was advised by Professors Paul Bogdan and Sérgio Pequito. I completed a one year appointment as a postdoctoral fellow at Johns Hopkins University with Professor Sridevi Sarma.
My research focuses on designing and analyzing novel control strategies, algorithms, and statistical learning tools to better understand complex dynamical networks, such as the brain, with a goal of designing next generation cyber-neural technology. My current research interests include control theory, complex networks, cyber-neural systems, and neuroscience. All of my publications are listed on Google Scholar.
In 2022, I was named a Rising Star in EECS and a 2022-2023 Ming Hsieh PhD Scholar at USC. In 2019, I was awarded the National Science Foundation Graduate Research Fellowship and the National Defense Science and Engineering Graduate Fellowship. Also in 2022, I was honored to receive the USC Women in Science and Engineering Leadership Award for starting the first PhD mentorship program for women in STEM at USC. In 2017, I received the USC Annenberg fellowship.
I am looking for motivated and talented Ph.D. students to join my research group. If you are interested, please reach out to me.
Control Theory
I am interested in deriving and applying traditional concepts in control to new classes of systems, such as fractional-order systems, to understand properties of biomedical, quantum, power, and even financial systems. By controlling these systems, we can make predictions and develop a deep understanding of them.
Networks
Networks pervade many systems we interact with on a daily basis. Studying networks is not only important for mitigating attacks, preventing disease, and securing assets, but it is also very exciting. I am interested in how to characterize, control, and measure large-scale networks.
Neurological Diseases
Understanding the brain is a grand challenge of the National Academy of Engineering. My research focuses on developing tools from systems and control as well as network science to unveil new insights into brain function/dysfunction. These insights are critical for developing treatments for neurological diseases such as epilepsy and Alzheimer's. In the future, these tools will be useful for treating other illnesses such as anxiety and depression.