Teaching Experience

Adjunct Lecturer, Lecturer, Senior Lecturer or Professor

  1. University Course: Cloud Computing and Big Data – Parallel and Scalable Machine Learning and Deep Learning, REI504M, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2020
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  2. University Course: High Performance Computing – Advanced Scientific Computing, REI105M, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2019
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  3. University Course: Cloud Computing and Big Data – Parallel and Scalable Machine Learning and Deep Learning, REI504M, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2018
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  4. University Course: High Performance Computing – Advanced Scientific Computing, REI105M, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2017
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  5. University Course: Cloud Computing and Big Data – Internet-based Shared Computing & Data Processing, REI504M, School of Engineering and Natural Sciences, University of Iceland, Iceland, Spring 2017
  6. University Course: High Performance Computing B – High Productivity Processing of Big Data, REI102F, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2015
  7. University Course: Statistical Data Mining, IÐN117F, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2015
  8. University Course: High Performance Computing A – Advanced Scientific Computing, REI101F, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2014
  9. University Course: Statistical Data Mining, IÐN117F, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2014
  10. University Course: High Performance Computing B – High Productivity Processing of Big Data, REI102F, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2013
  11. University Course: Statistical Data Mining, IÐN117F, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2013

Sessional Teacher with Supervision of Courses

  1. PRACE Tutorial: Parallel and Scalable Machine Learning, PRACE Advanced Training Center, Juelich Supercomputing Centre, Germany, February 17 – 19, 2020, Juelich, Germany
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  2. Artificial Intelligence Data Analysis School for Heliophysicists, Invited Training Course organized by the EC Project Artificial Intelligence Data Analysis (AIDA), CINECA, January 20-22, Bologna, Italy
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  3. Introduction to HPC and Quantum Computing for AI Applications, Invited Training Course organized by Obuda University and Parallel and Distributed Systems Lab, MTA Sztaki, December 13, Budapest, Hungary
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  4. Machine and Deep Learning Tutorial – Using High Performance Computing, Training Course organized by the ON4OFF Project, In-Telegence GmbH, December 5, 2019, Cologne, Germany
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  5. Introduction to Deep Learning Models, Training Course organized by the DEEP-EST Project, Juelich Supercomputing Centre, Germany, May 21 – 23, 2019, Juelich, Germany
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  6. PRACE Tutorial: Parallel and Scalable Machine Learning, PRACE Advanced Training Center, Juelich Supercomputing Centre, Germany, February 25 – 27, 2019, Juelich, Germany
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  7. DEEP-EST Tutorial: Machine Learning and Modular Supercomputing, HiPEAC – European Network on High Performance and Embedded Architecture and Compilation Conference, January 21 – 23, 2019, Valencia, Spain
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  8. Tutorial: Supercomputing and Big Data: Parallel and Scalable Machine Learning Algorithms, Tutorial, four course lectures, NextGen @ Helmholtz Conference 2018, July 25 – 27, 2018, GFZ Potsdam, Potsdam, Germany
  9. ISC2018 Tutorial on Machine Learning and Data Analytics, Invited Tutorial, Science, Technology, Engineering, and Mathematics (STEM) Student Day & Gala, International Supercomputing Conference (ISC), June 24 – 28, 2018, Frankfurt, Germany
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  10. DEEP-EST Tutorial: Introduction to Deep Learning, Seminar and Tutorial under the umbrella of the DEEP-EST EU Project, June 6 – 7, 2018, Juelich Supercomputing Centre, Germany
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  11. DEEP-EST Tutorial: Parallel and Scalable Machine Learning, Seminar and Tutorial under the umbrella of the DEEP-EST EU Project, March 6 – 8, 2018, Juelich Supercomputing Centre, Germany
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  12. Tutorial: Parallel and Scalable Machine Learning, Seminar and Tutorial under the umbrella of the PRACE EU Project, PRACE Advanced Training Center, January 15 – 17, 2018, Juelich Supercomputing Centre, Germany
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  13. Tutorial: Introduction to Parallel and Scalable Machine Learning, PRACE 2017 Spring School Big Data, Invited Lecture Series, three lectures, April 25 – 27, 2017, The Cyprus Institute, Nicosia, Cyprus
    [ EVENT ]
  14. Introduction to Machine Learning Algorithms, Invited University Lecture Series, six lectures, November 23 – 24, 2017, Ghent University, Belgium
  15. Deep Learning using a Convolutional Neural Network, Invited University Lecture Series, six lectures, November 30 – December 1, 2017, Ghent University, Belgium
  16. TUTORIAL: Einführung in Maschinelles Lernen zur Datenanalyse, Invited Tutorial, 2nd Smart Data Innovation Conference, October 10 – 12, 2017, Karlsruhe Institute of Technology (KIT), Germany
  17. TUTORIAL: Einführung in Maschinelles Lernen zur Datenanalyse, Invited Tutorial, 1st Smart Data Innovation Conference, October 12 – 13, 2016, Karlsruhe Institute of Technology (KIT), Germany
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  18. Machine Learning Tutorial for Supervised Classification using Support Vector Machines, Invited University Lecture Series, July 6 – July 8, 2016, University of Barcelona, Barcelona, Spain
  19. Data Analytics – Machine Learning – Tutorial, Invited Tutorial, Seminar Joint Laboratory for Extreme Scale Computing (JLESC) Summerschool, July 2 – 3, 2015, University of Barcelona, Barcelona, Spain
  20. University Course: Handling Large Datasets, RWTH Aachen University, Germany, 2009
  21. University Course: Distributed Systems, University of Applied Sciences Aachen, Germany, 2009
  22. University Course: Handling Large Datasets, RWTH Aachen University, Germany, 2008
  23. University Course: Grid Computing, University of Applied Sciences Aachen, Germany, 2008
  24. University Course: Distributed Systems, University of Applied Sciences Aachen, Germany, 2008
  25. University Course: Scientific Computing and Grid Computing, University of Applied Sciences Cologne, Germany, 2008
  26. University Course: Handling Large Datasets, RWTH Aachen University, Germany, 2007
  27. University Course: Grid Computing, University of Applied Sciences Aachen, Germany, 2007
  28. Introduction to UNICORE – Tutorial, Invited Tutorial, Open Middleware Infrastructure Institute for Europe (OMII-Europe) Training, July 11 – 12, 2007, University of Edinburgh, Edinburgh, UK
  29. University Course: Grid Computing, University of Applied Sciences Aachen, Germany, 2006
  30. Collaborative Online Visualization and Steering (COVS) Framework for e-Science Applications – Tutorial, Invited Tutorial, Distributed European Infrastructure for Supercomputing Applications (DEISA) Training – Special Topic: Performance and Portability, October 23 – 25, 2006, Forschungszentrum Juelich, Juelich Supercomputing Centre, Juelich, Germany

Supervision of Students and Thesis Opposition

Master’s Thesis

  1. Sigurðardóttir, S.B.: A Refactoring Catalogue and Tool for Refactoring C/C++ HPC Code, Advisor, Master Thesis, 60 ECTS, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2020
    [ MORE ] [ PDF (~1,63 MB) ] [ DEFENCE EVENT ]
  2. Guðmundsson, S.G.: Prediction of Time Series for Electricity Generation, Advisor, Master Thesis, 60 ECTS, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2020
    [ MORE ] [ PDF (~4,25 MB) ] [ DEFENCE EVENT ]
  3. Zhang, R.: Super-resolution of Sentinel-2 Images with Generative Adversarial Networks, Supervisor, Master Thesis, 60 ECTS, RWTH Aachen University of Technology, Aachen, Germany, 2020
    [ PDF (~37,00 MB) ]
  4. Barakat, C.: Modelling and Evaluation of Serial and Parallel Density-Based Clustering for Acute Respiratory Distress Syndrome, Supervisor, Master Thesis, 60 ECTS, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2019
    [ MORE ] [ PDF (~1,58 MB) ]
  5. Lange, J.: Translation of remote sensing images for the classification of unlabeled SAR data using Deep Convolutional Generative Adversarial Networks, Supervisor, Master Thesis, 60 ECTS, Faculty of Mathematics and Natural Sciences, Department of Physics, Humboldt University of Berlin, Germany, 2019
    [ PDF (~6,2 MB) ]
  6. Þrastarson, K.: Design, Implementation and Analysis of a Parallel and Scalable Cascade Support Vector Machine Framework, Supervisor, Master Thesis, 60 ECTS, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2018
    [ MORE ] [ PDF (~8,27 MB) ]
  7. Behrend, S.: Design, implementation, and optimization of an advanced I/O Framework for Parallel Support Vector Machines, Supervisor, Master Thesis, 60 ECTS, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2018
    [ MORE ] [ PDF (~4,55 MB) ]
  8. Sigurðardóttir, S.: Brain Image Classification with Support Vector Machines using Self-Dual Attribute Profiles, Supervisor, Master Thesis, 60 ECTS, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2018
    [ MORE ] [ PDF (~23,5 MB) ]
  9. Chhabra, N.: Generative Adversarial Networks for Image Anonymization, Supervisor, Master Thesis, 60 ECTS, RWTH Aachen University, Aachen, Germany, 2017
    [ PDF (~16,7 MB) ]
  10. Richerzhagen, M.: Design und Anwendung eines Skalierbaren Parallelen Künstlichen Neuronalen Netz, Supervisor, Master Thesis, 60 ECTS, University of Applied Sciences Aachen, Aachen, Germany, 2016
    [ MORE ] [ PDF (~2,16 MB) ]
  11. Glock, P.: Design and Evaluation of an SVM Framework for Scientific Data Applications, Supervisor, Master Thesis, 60 ECTS, University of Maastricht, Maastricht, The Netherlands, 2015
    [ MORE ] [ PDF (~1,10 MB) ]
  12. Klauck, S.: Task Core Mappings – Optimized MPI Process Placement for Blue Gene/Q Systems, Supervisor, Master Thesis, 60 ECTS, University of Potsdam, Hasso Plattner Institute, IT Systems Engineering, Potsdam, Germany, 2014
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  13. Holl, S.: Eclipse-based Client Support for Scientific Biological Applications in e-Science Infrastructures, Supervisor, Master Thesis, 60 ECTS, University of Duesseldorf, Institute of Informatics, Duesseldorf, Germany, 2008
    [ MORE ] [ PDF (~2,45 MB) ]

Doctoral Thesis

  1. Memon, M.S.: Standards-based Models & Architectures to Automate Scalable & Distributed Data Processing & Analysis, Supervisor, Doctoral Thesis, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2019
    [ OPINVISINDI ] [ PDF (~7,03 MB) ] [ MORE ]
  2. Goetz, M.: Scalable Data Analysis in High Performance Computing, Supervisor, Doctoral Thesis, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2017
    [ OPINVISINDI ] [ JUSER ] [ PDF (~13,57 MB) ] [ MORE ]

Member of Doctoral Board

  1. Cavallaro, G.: Spectral-Spatial Classification of Remote Sensing Optical Data with Morphological Attribute Profiles using Parallel and Scalable Methods, Member of Doctoral Board, Doctoral Thesis, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2016
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