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We are happy to announce that we demonstrated our latest work titled “VISPool: Enhancing Transformer Encoders with Vector Visibility Graph Neural Networks” at ACL 2024, Bangkok/Thailand!   In our #ACL2024 paper, we explore the powerful combination of transformers and graph neural networks (GNNs) to push the boundaries of natural language processing (NLP). While transformers have […]

We are pleased to announce that our paper on generalizing the fractional Fourier transform to the graph domain titled “Graph Fractional Fourier Transform: A Unified Theory” published in IEEE Transactions on Signal Processing! Highlights of our contributions include: A rigorous extension of the fractional power-based definition of GFRFT to support any graph structure and transform […]

Happy to share our latest paper titled “Text-RGNNs: Relational Modeling for Heterogeneous Text Graphs” published in IEEE Signal Processing Letters! Building on the foundational Text-graph convolutional Network (TextGCN), which represents corpus with heterogeneous text graphs, we addressed a key limitation: GCNs are inherently designed to operate within homogeneous graphs, potentially limiting their performance. To overcome […]

    UMRAM had broad participation in the 2024 ISMRM & ISMRT Annual Meeting & Exhibition held 04-09 May 2024 in Singapore. Two educational lectures as well as five oral, one power pitch and two digital poster presentations were delivered by UMRAM faculty and students.   Educational lecture: Dr. Ergin Atalar: “Gradient Coil Design” Dr. […]

Prof. Tolga Çukur of the Department of Electrical and Electronics Engineering and director of UMRAM has been named a fellow of International Society for Magnetic Resonance in Medicine (ISMRM). ISMRM is an international, nonprofit, scientific association whose purpose is to promote communication, research, development and applications in the field of magnetic resonance in medicine and […]