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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
Balanced Graph Partition Refinement using the Graph p-Laplacian
Published in PASC' 18: Proceedings of the Platform for Advanced Scientific Computing Conference., 2018
Simpson T, Pasadakis D, Kourounis D, Fujita K, Yamaguchi T, Ichimura T, Schenk O (2018) 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 https://doi.org/10.1145/3218176.3218232
Block-enhanced precision matrix estimation for large-scale datasets
Published in Journal of Computational Science, 2021
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, 101389, ISSN 1877-7503 https://doi.org/10.1016/j.jocs.2021.101389
Large-Scale Precision Matrix Estimation With SQUIC
Published in SSRN, 2021
A. Eftekhari, L. Gaedke-Merzhäuser, D. Pasadakis, M. Bollhöfer, S. Scheidegger, and O. Schenk, "Large-Scale Precision Matrix Estimation With SQUIC" (August 12, 2021). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3904001
Multiway p-spectral graph cuts on Grassmann manifolds
Published in Machine Learning, 2022
D. Pasadakis, C.L. Alappat, O. Schenk and G. Wellein, "Multiway p-spectral graph cuts on Grassmann manifolds," in Machine Learning 111, 791–829 (2022). https://link.springer.com/article/10.1007/s10994-021-06108-1
Sparse Quadratic Approximation for Graph Learning
Published in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
D. Pasadakis, M. Bollhöfer and O. Schenk, "Sparse Quadratic Approximation for Graph Learning," in IEEE Transactions on Pattern Analysis and Machine Intelligence. https://ieeexplore.ieee.org/document/10091452
talks
A historical perspective of Graph Theory
Published:
teaching
High Performance Topology Optimization
Semester project, The Platform for Advanced Scientific Computing (PASC) Conference, 2018
Supervising the MSc students Ezekiel Barnett, Sumeet Gyanchandani, and Sameer Rawat on a semester project and a poster submission as part of the course Software Atelier: Simulation, Data Science & Supercomputing. The poster won the 3rd place runner-up award for “Best poster” at PASC’18, Basel, Switzerland.
Poster
A High Performance Video Segmentation Framework
Semester project, Università della Svizzera italiana (USI), 2019
Supervising the MSc students Liudmila Karagyaur, Vanessa Braglia, and Lorenzo Ferri on a semester project as part of the course Software Atelier: Simulation, Data Science & Supercomputing.
Report
Linear Algebra
Bachelor's course, Università della Svizzera italiana (USI), 2020
Course syllabus
Linear mappings,linear spaces, Gauss method, vector spaces, linear maps and matrices, determinants, eigenvectors and eigenvalues. Course Link
A study of Spectral Clustering techniques for machine learning applications
Bachelor's thesis, Università della Svizzera italiana (USI), 2020
Supervising the Bachelor student Lazar Najdenov on his thesis. Final grade: 10/10.
Thesis
High Performance Computing
MSc course, Università della Svizzera italiana (USI), 2021
Course syllabus
Numerical methods and HPC, large-scale scientific simulations, parallel programming models, HPC systems,scientific mathematical libraries, C programming language, linear algebra, mathematical optimization, partial differential equations. Course Link
Numerical Computing
Bachelor's course, Università della Svizzera italiana (USI), 2022
Course syllabus
Graph clustering, graph partitioning, solving linear systems of equations, page rank algorithm and large-scale nonlinear optimization, real-world applications. Course Link
High-Performance Computing Lab for CSE
Bachelor's course, ETH Zürich, 2022
Course syllabus
Computational Science and Engineering, HPC systems, parallel programming models, large-scale scientific simulations, performance analysis, parallelism detection, OpenMP, MPI, scientific mathematical libraries. Course Link
Spectral clustering using a multilevel approach
Semester project, ETH Zürich, 2022
Supervising the MSc student Kalman Szenes on his semester project.
Slides