Selin Aviyente

About

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.

Education

• Ph.D., Electrical Engineering, University of Michigan 2002
• M.S., Electrical Engineering, University of Michigan 1999
• B.S., Electrical Engineering, Bogazici University 1997

Selected publications

• E. Al-Sharoa, M. Alkhassaweneh and S. Aviyente, 'Detecting and Tracking Community Structure in Temporal Networks: A Low-Rank + Sparse Estimation Based Evolutionary Clustering Approach,' in IEEE Transactions on Signal and Information Processing over Networks, in press.
• T. Munia and S. Aviyente, 'Time-Frequency Based Phase-Amplitude Coupling Measure For Neuronal Oscillations'. Scientific Reports, 2019.
• T. T. K. Munia and S. Aviyente, 'Graph-to-signal transformation based classification of functional connectivity brain networks,' PLOS ONE, 2019.
• E. Al-Sharoa, M. Alkhassaweneh and S. Aviyente, 'Tensor Based Temporal and Multi-layer Community Detection for Studying Brain Dynamics During Resting State fMRI,' IEEE Transactions on Biomedical Engineering, vol. 66, no. 3, 2019.
• A. Zare, A. Ozdemir, M. Iwen and S. Aviyente, 'Extension of PCA to higher order data structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA,' Proceedings of the IEEE, vol. 106, no. 8, 2018.