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个人简介
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.
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.
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INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY (2024): 112299-112299
IEEE ACCESS (2024): 6423-6436
Mert Indibi,Selin Aviyente
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)pp.7035-7039, (2024)
CoRR (2024)
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Mert Indibi,Selin Aviyente
2023 International Conference on Sampling Theory and Applications (SampTA)pp.1-5, (2023)
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Symmetryno. 1368 (2023): 1368-1368
ISBIpp.1-5, (2023)
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ICASSP Workshopspp.1-5, (2023)
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