Research

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. 

News

• October 2026: New paper 'Spatio -temporal anomaly detection: A regularized robust tensor decomposition with graph total variation and grouped sparsity' published in Signal Processing.

• September 2026: New paper 'Multiplex Community Detection for Subgroup Identification Within Functional Connectivity Networks' published in IEEE Transactions on Biomedical Engineering.

• September 2026: New paper 'Co-Hub Node Based Multiview Graph Learning with Theoretical Guarantees' published in IEEE Transactions on Signal Processing.

• July 2026: S. Aviyente was an invited speaker at SIU 2026, Istanbul.

• May 2026: Two papers presented at ICASSP 2026.

• November 2025: New paper 'Brain Connectivity: From network science to tensor models' published in IEEE Signal Processing Magazine.

• 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.

Selected Publications

Tensor Decomposition and Applications:

• • • M. Indibi and S. Aviyente, "Spatio -temporal anomaly detection: A regularized robust tensor decomposition with graph total variation and grouped sparsity," Signal Processing, vol. 247, 2026.

    • 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.

Network Science and Community Detection:

• • • B. Hunyadi and S. Aviyente, "Brain Connectivity: From network science to tensor models", IEEE Signal Processing Magazine, vol. 42, no. 4, pp. 8-24, 2025.

    • E. Al-Sharoa, M. Al-Wardat and S. Aviyente, "Community Detection in Multi-Aspect Functional Brain Networks: Robust Tensor Decomposition Approach," IEEE Access, 2025.

    • 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.

Graph Signal Processing:

• • • B. Banerjee, M. Al-Wardat, T. Maiti and S. Aviyente, "Co-Hub Node Based Multiview Graph Learning with Theoretical Guarantees," IEEE Transactions on Signal Processing, 2026.

    • M. Ortiz-Bouza, D. Vu, A. Karaaslanli and S. Aviyente, "Learning Graph Filters for Structure-Function Coupling Based Hub Node Identification," IEEE Transactions on Signal and Information Processing over Networks, vol. 11, pp. 980-993, 2025.

    • M. Ortiz-Bouza and S. Aviyente, "Learning Optimal Graph Filters for Clustering of Attributed Graphs," IEEE Transactions on Signal and Information Processing over Networks, vol. 11, pp. 520-534, 2025. 

    • A. Karaaslanli and S. Aviyente, "Multiview Graph Learning with Consensus Graph," IEEE Transactions on Signal and Information Processing over Networks, vol. 11, pp. 161-176, 2025.

Computational Neuroscience:

• • • 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.

Current Projects 

• •  NSF-SNSF:Learning disentangled graph representations for biomedicine, NSF, PI

  • CIF: Small: Multiview Graph Learning with Applications to Single Cell Gene Expression Networks , NSF, PI (co-PI: T. Maiti, Statistics).

Service

• • 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.

Students

• • Mert Indibi (Ph.D.)

  • Utku Guven (Ph.D.)

  • Sema Athamnah (Ph. D.)

  • Mohammad Alwardat (Ph.D.)

  • Gul Efsan Ozbekoglu (Undergraduate research assistant)

  • Efe Karakoca (Undergraduate summer intern)