Selin Aviyente

Professor, Associate Chair for Undergraduate Studies

Department of Electrical and Computer Engineering

Michigan State University, East Lansing, MI 48824.

aviyente@egr.msu.edu

(517) 355 7649

 

I am a professor at the Electrical and Computer Engineering Department, Michigan State University.  I am also affiliated with the Communications and Signal Processing Lab at Michigan State University (CSP Group).

CV

Research

My research focuses on the theory and applications of statistical signal processing, in particular non-stationary signal analysis. I work on a variety of topics including: 1) transform based signal processing with an emphasis on sparsity-constrained feature extraction and classification; 2) signal processing methods for studying functional connectivity brain networks; 3) signal processing on graphs and networks; and 4) higher-order data analysis.

 

More recently, my research focuses on high-dimensional signal processing, in particular tensor representation of large volumes of data. In this area, we have developed efficient tensor decomposition methods and focused on the applications of tensor based methods for community detection in multi-layer networks such as the dynamic functional connectivity networks of the brain.

 

 

 

Software

 

  • Time-frequency based Phase Amplitude Coupling Measure (tf-PAC)
  • Sparse Representations for Classification (srsc.zip)

 

Current Projects

 

·         ATD: Next Generation Statistical Learning Theory and Methods for Multimodal Spatio-Temporal Data with Application to Computer Vision, NSF, co-PI (PI: T. Maiti, Statistics), $515,526, 08/15/19-07/31/22.

·         CIF: Small: Low-Dimensional Structure Learning for Tensor Data with Applications to Neuroimaging, NSF, PI (co-PI: M. Iwen, Math), $500,000, 07/01/16-06/30/20.

·         Cognitive Control in Anxiety: The Role of Ovarian Hormones, NIH, co-PI (PI: J. Moser), $3,381,819, 3/25/2016 - 2/28/2021.

 

Service

 

·         Senior Area Editor, IEEE Transactions on Signal Processing, 2015-present.

·         Associate Editor, IEEE Transactions on Signal and Information Processing Over Networks, 2017-present.

·         Associate Editor, Digital Signal Processing, 2018-present.

·         Member, IEEE Signal Processing Society, SPTM Committee

·         Member, IEEE Signal Processing Society, BISP Committee

 

Selected Recent Publications 

 

Tensor Decomposition and Applications:

 

·         S. E. Sofuoglu and S. Aviyente, “Multi-Branch Tensor Network Structure for Tensor-Train Discriminant Analysis,” submitted.

·         E. Al-Sharoa, M. Alkhassaweneh and S. Aviyente, “Tensor Based Temporal and Multi-layer Community Detection for Studying Brain Dynamics During Resting State fMRIIEEE 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.

·         A. G. Mahyari, D. Zoltowski, E. M. Bernat and S. Aviyente, “A tensor decomposition based approach for detecting dynamic network states from EEGIEEE Transactions on Biomedical Engineering, vol. 64, no. 1, pp. 225-237, 2017.

 

Network Science and Community Detection:

 

·         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. T. K. Munia and S. Aviyente, “Graph-to-signal transformation based classification of functional connectivity brain networks,” PLOS ONE, 2019.

·         A. Ozdemir, E. M. Bernat and S. Aviyente, “Recursive Tensor Subspace Tracking for Dynamic Brain Network AnalysisIEEE Transactions on Signal and Information Processing on Networks, vol. 3, no. 4, pp. 669-682, 2017.

·         A. Ozdemir, M. Bolanos, E. M. Bernat and S. Aviyente, “Hierarchical Spectral Consensus Clustering for Group Analysis of Functional Brain NetworksIEEE Transactions on Biomedical Engineering, vol. 62, no. 9, pp. 2158-2169, 2015.

 

 

Phase Synchrony and Functional Connectivity:

·         T. Munia and S. Aviyente, “Time-Frequency Based Phase-Amplitude Coupling Measure For Neuronal Oscillations”, Scientific Reports, 2019.

·         M. Al-Khassaweneh, M. Villafane-Delgado, A. Y. Mutlu, and S. Aviyente, “A Measure of Multivariate  Phase Synchrony using Hyperdimensional GeometryIEEE Transactions on Signal Processing, pp. 2774-2787, vol. 64, no. 11, 2016.  

·         T. P. Moran, E. M. Bernat, S. Aviyente, H. S. Schroder and J. Moser, “Sending Mixed Signals: Worry is associated with enhanced initial error processing but reduced call for subsequent cognitive control,” Social, Cognitive and Affective Neuroscience, 10 (11), pp. 1548-1556, 2015.

·         S. Aviyente and A. Y. Mutlu, “A Time-Frequency Based Approach to Phase and Phase Synchrony Estimation,” IEEE Transactions on Signal Processing, vol. 59, no. 7, pp. 3086-3098, 2011.

·         S. Aviyente, E. M. Bernat, W. S. Evans and S. R. Sponheim, “A phase synchrony measure for quantifying dynamic functional integration in the brain,” vol. 32, no. 1, pp. 80-93, Human Brain Mapping, 2011.

 

Directed Information:
 
·         Y. Liu, J. Moser and S. Aviyente, “Community detection for directional neural networks inferred from multichannel multi-subject EEG dataIEEE Transactions on Biomedical Engineering, vol. 61, no. 7, pp. 1919-1930, 2014.

·         Y. Liu and S. Aviyente, “Quantification of Effective Connectivity in the Brain Using a Measure of Directed InformationComputational and Mathematical Methods in Medicine, 2012.

 

Signal Processing Approaches in Fault Diagnosis:

·         R. K. Singleton II, E. G. Strangas and S. Aviyente, “The Use of Bearing Currents and Vibrations in Lifetime Estimation of BearingsIEEE Transactions on Industrial Informatics, vol. 13, no. 3, 2017.

·         R. K. Singleton II, E. G. Strangas and S. Aviyente, “Extended Kalman Filtering for Remaining Useful Life Estimation of BearingsIEEE Transactions on Industrial Electronics, vol. 62, no. 3, pp. 1781-1790, 2015.

 

Current Students

 

·         Emre Sofuoglu (Ph.D.)

·         Abdullah Karaaslanli (Ph.D.)

·         Tamanna Tabassum Khan Munia (Ph.D.)

·         Zoe Dittman (B.S.)

 

Past Students

 

·         Ke Huang, Ph.D. 2007, currently at Google

·         Mahmood Al-Khassaweneh, Ph.D. 2007, currently Associate Professor at Yarmouk University, Jordan

·         Zeyong Shan, Ph.D. 2008, currently at Safety Vision

·         Jacob Swary, M.S. 2007

·         Westley Evans, M.S. 2008

·         Marcos Bolanos, Ph.D. 2012, currently at CNA

·         Ying Liu, Ph.D. 2012, currently at Bosch, Pittsburgh, PA

·         Ali Yener Mutlu, Ph.D. 2012, currently Senior Data Scientist at SIXT, Germany.

·         Suhaily Cardona, M.S. 2012, currently at Applied Physics Lab, Baltimore, MD.

·         Arash Mahyari, Ph.D. 2016, currently at ABB R&D Center, CA.

·         Rodney Singleton, Ph.D. 2016, currently at Applied Physics Lab, Baltimore, MD.

·         Marisel Villafane-Delgado, Ph.D. 2017, currently at Applied Physics Lab, Baltimore, MD.

·         Alp Ozdemir, Ph.D. 2017, currently at GM, Detroit, MI.

·         Esraa Al-Sharoa, Ph.D. 2018, currently at Jordan University of Science and Technology (JUST), Jordan.

 

Recent Teaching

·         ECE 280, Mathematical Methods in Electrical Engineering (Spring 2017)

·         ECE 366, Introduction to Signal Processing (Spring, Fall 2019)

·         ECE 466, Digital Signal Processing and Filter Design (Spring 2017)

·         ECE 866, Time-Frequency and Wavelet Analysis (Spring 2018)