You can also find my articles on my scholar profile.

Journal articles

  • A. Eftekhari, L. Gaedke-Merzhäuser, D. Pasadakis, M. Bollhöfer, S. Scheidegger, and O. Schenk. 2024. “Algorithm XXX: Sparse Precision Matrix Estimation With SQUIC,” ACM Transactions of Mathematical Software, March 2024. doi: 10.1145/3650108.

  • D. Pasadakis, M. Bollhöfer, and O. Schenk, “Sparse quadratic approximation for graph learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 11256-11269, 1 Sept. 2023, doi: 10.1109/TPAMI.2023.3263969.

  • D. Pasadakis, C.L. Alappat, O. Schenk, and G. Wellein, “Multiway p-spectral graph cuts on Grassmann manifolds”. Machine Learning 111, 791–829, 2022, doi: 10.1007/s10994-021-06108-1.

  • A. Eftekhari, D. Pasadakis, M. Bollhöfer, S. Scheidegger, and O. Schenk, “Block-enhanced precision matrix estimation for large-scale datasets”, Journal of Computational Science, Volume 53, 2021, doi: 10.1016/j.jocs.2021.101389.

Conference papers

  • D. Pasadakis, O. Schenk, V. Vlacic, and A.-J. Yzelman, “Nonlinear spectral clustering with C++ GraphBLAS”. Accepted at IEEE High Performance Extreme Computing Conference, 25 - 29 September 2023. Outstanding short paper award. paper poster

  • V.I. Makri, D. Pasadakis, and N. Pasadakis, “A novel chemometric approach for oil & source rock clustering”, in European Association of Geoscientists & Engineers, pp. 1-2, 2023, doi: 10.3997/2214-4609.202333183.

  • T. Simpson, D. Pasadakis, D. Kourounis, K. Fujita, T. Yamaguchi, T. Ichimura, and O. Schenk. “Balanced graph partition refinement using the graph p-Laplacian,” in Proceedings of the Platform for Advanced Scientific Computing Conference, Association for Computing Machinery, New York, NY, USA, PASC ’18, 2018, doi: 10.1145/3218176.3218232.

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