Professor
Department of Electrical and Computer Engineering
Michigan State University
Phone: (517) 355 7649
Email: aviyente@msu.edu
CV (updated October 2024)
My research focuses on the theory and applications of statistical signal processing and machine learning. 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 on graphs and networks; 3) higher-order data analysis and 4) applications in computational neuroscience.
My current research interests are in the area of low-dimensional structure learning from high-dimensional Euclidean and non-Euclidean data. In this area, we focus on tensor representation of large volumes of data and develop computationally efficient tensor decomposition methods. For dimensionality reduction in non-Euclidean data, we focus on community detection in multi-layer networks such as the dynamic functional connectivity networks of the brain.
Most of the code from my group can be found at https://github.com/SPLab-aviyente.
May 2025: New paper 'Community Detection in Multi-Aspect Functional Brain Networks: Robust Tensor Decomposition Approach' published in IEEE Access.
April 2025: Three papers accepted for presentation at SSP 2025.
January 2025: Our paper 'Multiview Graph Learning with Consensus Graph' published in IEEE Transactions on Signal and Information Processing over Networks.
August 2024: New NSF grant: NSF-SNSF:Learning disentangled graph representations for biomedicine, joint project with Dr. Dorina Thanou from EPFL.
January 2024: New paper 'Community Detection in Multiplex Networks Based on Orthogonal Nonnegative Tri-Matrix Factorization' published in IEEE Access.
June 2023: New NSF grant: Collaborative Research: CIF: Medium: Robust Learning over Graphs
June 2023: New paper in IEEE Signal Processing Magazine, 75th Anniversary of SPS Special Issue
Feb 2023: Call for Papers: IEEE Satellite Workshop: Data Science and Learning Workshop: Unraveling the Brain
Feb 2023: Three papers accepted for presentation at ICASSP 2023.
October 2022: Graduate research assistant position available for Fall 2023.
July 2022: Our paper "Explainability in Graph Data Science: Interpretability, replicability, and reproducibility of community detection" has been published in IEEE Signal Processing Magazine.
May 2022: S. Aviyente gave a talk on "Multiview Graph Learning" as part of the One World MINDS Seminar series.
May 2022: New NSF grant: CIF: Small: Multiview Graph Learning with Applications to Single Cell Gene Expression Networks
P. Li, S. E. Sofuoglu, S. Aviyente and T. Maiti, "Coupled support tensor machine classification for multimodal neuroimaging data," Statistical Analysis and Data Mining, pp. 797-818, 2022.
S. E. Sofuoglu and S. Aviyente,"GLOSS: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data," Signal Processing, vol. 192, 2022.
S. E. Sofuoglu and S. Aviyente, “Multi-Branch Tensor Network Structure for Tensor-Train Discriminant Analysis,” IEEE Transactions on Image Processing, vol. 30, pp. 8926-8938, 2021.
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.
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, 2017.
M. Ortiz-Bouza and S. Aviyente, "Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization," IEEE Access, 2024.
E. Al-Sharoa and S. Aviyente, "Community Detection in Fully-Connected Multi-layer Networks through Joint Nonnegative Matrix Factorization," IEEE Access, 2022.
S. Aviyente, "A signal processing perspective to community detection in dynamic networks," Digital Signal Processing, 2021.
A. Karaaslanli and S. Aviyente, "Community Detection in Dynamic Networks: Equivalence Between Stochastic Blockmodels and Evolutionary Spectral Clustering," IEEE Transactions on Signal and Information Processing over Networks, vol. 7, pp. 130-143, 2021.
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,” IEEE Transactions on Signal and Information Processing over Networks, vol. 5, no. 4, 2019.
A. Ozdemir, E. M. Bernat and S. Aviyente, “Recursive Tensor Subspace Tracking for Dynamic Brain Network Analysis,” IEEE 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 Networks,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 9, pp. 2158-2169, 2015.
Y. Liu, J. Moser and S. Aviyente, “Community detection for directional neural networks inferred from multichannel multi-subject EEG data,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 7, pp. 1919-1930, 2014.
J. S. Moser, T. Munia, C. C. Louis and S. Aviyente,"Errors elicit frontoparietal theta-gamma coupling that is modulated by endogenous estradiol levels," International Journal of Psychophysiology, 2024.
G. A. Buzzell, Y. Niu, S. Aviyente and E. M. Bernat, "A practical introduction to EEG Time-Frequency Principal Components Analysis (TF-PCA)," Developmental Cognitive Neuroscience, vol. 55, June 2022.
T. Munia and S. Aviyente, "Multivariate Analysis of Bivariate Phase-Amplitude Coupling in EEG Data Using Tensor Robust PCA," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1268-1279, 2021.
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.
M. Al-Khassaweneh, M. Villafane-Delgado, A. Y. Mutlu, and S. Aviyente, “A Measure of Multivariate Phase Synchrony using Hyperdimensional Geometry,” IEEE 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.
CIF: Small: Multiview Graph Learning with Applications to Single Cell Gene Expression Networks , NSF, PI (co-PI: T. Maiti, Statistics).
CIF: Small: Community Detection in Multilayer Networks with Applications to Functional Connectivity Brain Networks, NSF, PI (co-PI: T. Maiti, Statistics).
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).
Steering Committee Member, IEEE Brain Technical Committee
Steering Committee Member, Data Science Initiative, IEEE Signal Processing Society
Senior Area Editor, IEEE Transactions on Signal and Information Processing Over Networks, 2025-present.
Senior Area Editor, IEEE Signal Processing Magazine, 2024-present.
Mert Indibi (Ph.D.)
Meiby Ortiz-Bouza (Ph.D.)
Sema Athamnah (Ph. D.)
Mohammad Alwardat (Ph.D.)
Duc Vu (Undergraduate research assistant)
Nathan Marchywka (Undergraduate research assistant)