Her research focuses on the theory and applications of statistical signal processing, in particular non-stationary signal analysis. She is interested in developing methods for efficient signal representation, detection and classification. Dr. Aviyente is also interested in the applications of signal processing to biological signals such as the analysis of event related brain potentials. Her current research focuses on the study of the functional networks in the brain.
Withrow Teaching Excellence Award
NSF Career Award, 2008
Ph.D., Electrical Engineering, University of Michigan 2002
M.S., Electrical Engineering, University of Michigan 1999
B.S., Electrical Engineering, Bogazici University 1997
A. Ozdemir, E. M. Bernat and S. Aviyente,”Recursive Tensor Subspace Tracking for Dynamic Brain Network Analysis,” IEEE Transactions on Signal and Information Processing over Networks, in press.
R. K. Singleton, E. G. Strangas and S. Aviyente, “The Use of Bearing Currents and Vibrations in Lifetime Estimation of Bearings,” IEEE Transactions on Industrial Informatics, vol. 13, no. 3, June 2017.
A. G. Mahyari and S. Aviyente, “Simultaneous Sparse Approximation and Common Component Extraction using Fast Distributed Compressive Sensing,” Digital Signal Processing, vol. 60, pp. 230-241, January 2017.
A. G. Mahyari, D. Zoltowski, E. M. Bernat and S. Aviyente, “A tensor decomposition based approach for detecting dynamic network states from EEG,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 1, pp. 225-237, January 2017.
S. Aviyente, A. Tootell, E. M. Bernat, “Time-frequency phase synchrony approaches with ERPs,” International Journal of Psychophysiology, vol. 111, pp. 88-97, January 2017.