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

• 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 

• May 2022: Our paper "Coupled support tensor machine classification for multimodal neuroimaging data," has been published in Statistical Analysis and Data Mining.

• April 2022: Abdullah Karaaslanli was selected as Outstanding Graduate Student in Electrical Engineering. Congratulations!

• April 2022: Our paper "Graph Regularized Low-Rank Tensor-Train for Robust Principal Component Analysis" has been published in IEEE Signal Processing Letters.

• April 2022: Our paper "scSGL: Kernelized Signed Graph Learning for Single-Cell Gene Regulatory Network Inference" has been accepted for publication in Bioinformatics.

• October 2021: Our paper "Multi-branch tensor network structure for tensor-train discriminant analysis," has been accepted for publication in IEEE Transactions on Image Processing.

• October 2021: Our paper "GLOSS: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data," has been accepted for publication in Signal Processing.

Selected Publications

Tensor Decomposition and Applications:

• • • 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.

Network Science and Community Detection:

• • •  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.

Computational Neuroscience:

• • • 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 

• • 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).

Service

• • Chair, IEEE Signal Processing Society, BISP Committee

  • Steering Committee Member, IEEE Brain Technical Committee

  • Steering Committee Member, Data Science Initiative, IEEE Signal Processing Society

  • Member, IEEE Signal Processing Society, SPTM Committee

  • Associate Editor, IEEE Open Journal of Signal Processing, 2020-present.

  •  Associate Editor, IEEE Transactions on Signal and Information Processing Over Networks, 2017-2021.

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

Students

• • Abdullah Karaaslanli (Ph.D.)

  • Mert Indibi (Ph.D.)

  • Meiby Ortiz-Bouza (Ph.D.)

  • Duc Vu (Undergraduate research assistant)

  • Brooke Osterkamp (Undergraduate research assistant)