Logo: University of Southern California
Neural Systems Engineering & Information Processing Lab

Maryam M. Shanechi
Assistant Professor & Viterbi Early Career Chair

Ming Hsieh Department of Electrical Engineering - Systems
Viterbi School of Engineering

University of Southern California
shanechi@usc.edu       Office: EEB408






Maryam Shanechi is recognized as one of the Popular Science Brilliant 10. Read more here.


Our paper first authored by Yuxiao Yang in the IEEE EMBC is selected as the geographic finalist from North America, and is announced as the top three winners of the best student paper competition. Congratulations Yuxiao! Read more here.


Maryam Shanechi is selected by the National Academy of Engineering (NAE) for the 2015 US Frontiers of Engineering (FOE) symposium. Read more here.


NSEIP lab recieves an inaugural Cal-BRAIN Award as part of California's new grant program to develop neurotechnologies that revolutionize the understanding of the brain. Read more here.


Maryam Shanechi is appointed as the inaugural holder of a Viterbi Early Career Chair.


Maryam Shanechi receives the NSF CAREER Award. Read the USC news story here.


Our research has been selected by Google Solve for X as a "Tech Moonshot". You can see our moonshot page and video here.


Watch a 3 minute video summarizing our research at the MIT Technology Review EmTech conference here.


Maryam Shanechi is selected as a Pioneer in the MIT Technology Review Innovators Under 35 list for her work on brain-machine interfaces. See the MIT Technology Review story here and the USC story here.


Our paper on a brain-machine interface for control of medical coma wins the best paper award in Technology, Computing, and Simulation at the 2014 International Anesthesia Research Society (IARS) annual meeting.


Our Nature Communication paper on a cortical-spinal prosthesis is now online and has been highlighted in various media outlets including Cornell Chronicle, BBC news, and Le Monde.


Our PLoS Computational Biology paper on a brain-machine interface for automatic control of anesthesia is now online and has been highlighted in various media outlets including Cornell Chronicle, MIT news, and NBC news


Our PLoS ONE paper on a brain-machine interface to jointly decode the target and trajectory of movement using an optimal feedback control design is now online.


Our Nature Neuroscience paper on a brain-machine interface for enhanced sequential motor function is now online and has been highlighted in various outlets including Nature, MIT EECS News, and Discovery News.


Our IEEE TNSRE paper on a decoder for goal-directed movements from neural signals is now online.

Our laboratory develops algorithmic solutions to problems in basic and clinical neuroscience that involve the collection and manipulation of neural signals. Our work combines algorithm development and modeling with in vivo experimental implementation and testing, and is conducted in close collaboration with a variety of experimental labs. One problem of particular interest to our lab is the design of closed-loop brain-machine interface (BMI) architectures.

In our research we use the principles of information and control theories, statistical inference, and signal processing to gain insight into basic neuroscience questions and to develop effective solutions for clinical neuroscience problems. Our work includes the development of BMIs that aim to restore original motor function in disabled patients, and BMIs that have the potential to also enhance such original motor function. These BMIs record the neural activity in the relevant brain areas and use diverse mathematical tools to infer from this activity the motor intent of the user. We also develop BMIs for automatic closed-loop control of the brain state under anesthesia that adjust the real-time anesthetic infusion rate based on non-invasive neural recordings. Finally, we design BMIs for closed-loop brain stimulation to treat neurological diseases.

Open Postions

Postdoctoral Position Available!  

We're recruiting postdoctoral scholars with control and signal processing background to develop closed-loop control algorithms for neural systems. Applicants with prior experience in stochastic control are especially encouraged to apply. The project involves both theoretical development and algorithm design, and validation in real-time control experiments with human recordings. Interested candidates should apply by sending their CV to shanechi@usc.edu.

Graduate Student Openings! 

We're recruiting PhD students with strong mathematical background who are interested in developing signal processing and control-theoretic algorithms for neural engineering applications. Interested candidates should contact shanechi@usc.edu.