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, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2018
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  2. University Course: High Performance Computing – Advanced Scientific Computing, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2017
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  3. University Course: Cloud Computing and Big Data – Internet-based Shared Computing & Data Processing, School of Engineering and Natural Sciences, University of Iceland, Iceland, Spring 2017
  4. University Course: High Performance Computing B – High Productivity Processing of Big Data, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2015
  5. University Course: Statistical Data Mining, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2015
  6. University Course: High Performance Computing A – Advanced Scientific Computing, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2014
  7. University Course: Statistical Data Mining, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2014
  8. University Course: High Performance Computing B – High Productivity Processing of Big Data, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2013
  9. University Course: Statistical Data Mining, 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 25 – 27, 2019, Juelich, Germany
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  2. 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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  3. 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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  4. 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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  5. 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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  6. 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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  7. Introduction to Machine Learning Algorithms, Invited University Lecture Series, six lectures, November 23 – 24, 2017, Ghent University, Belgium
  8. Deep Learning using a Convolutional Neural Network, Invited University Lecture Series, six lectures, November 30 – December 1, 2017, Ghent University, Belgium
  9. TUTORIAL: Einführung in Maschinelles Lernen zur Datenanalyse, Invited Tutorial, 2nd Smart Data Innovation Conference, October 10 – 12, 2017, Karlsruhe Institute of Technology (KIT), Germany
  10. 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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  11. Machine Learning Tutorial for Supervised Classification using Support Vector Machines, Invited University Lecture Series, July 6 – July 8, 2016, University of Barcelona, Barcelona, Spain
  12. 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
  13. University Course: Handling Large Datasets, RWTH Aachen University, Germany, 2009
  14. University Course: Distributed Systems, University of Applied Sciences Aachen, Germany, 2009
  15. University Course: Handling Large Datasets, RWTH Aachen University, Germany, 2008
  16. University Course: Grid Computing, University of Applied Sciences Aachen, Germany, 2008
  17. University Course: Distributed Systems, University of Applied Sciences Aachen, Germany, 2008
  18. University Course: Scientific Computing and Grid Computing, University of Applied Sciences Cologne, Germany, 2008
  19. University Course: Handling Large Datasets, RWTH Aachen University, Germany, 2007
  20. University Course: Grid Computing, University of Applied Sciences Aachen, Germany, 2007
  21. University Course: Grid Computing, University of Applied Sciences Aachen, Germany, 2006

Supervision of Students and Thesis Opposition

Master’s Thesis

  1. Þrastarson, K.: Design, Implementation and Analysis of a Parallel and Scalable Cascade Support Vector Machine Framework, Master Thesis, 60 ECTS, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2018
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  2. Behrend, S.: Design, implementation, and optimization of an advanced I/O Framework for Parallel Support Vector Machines, Master Thesis, 60 ECTS, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2018
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  3. Sigurðardóttir, S.: Brain Image Classification with Support Vector Machines using Self-Dual Attribute Profiles, Master Thesis, 60 ECTS, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2018
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  4. Richerzhagen, M.: Design und Anwendung eines Skalierbaren Parallelen Künstlichen Neuronalen Netz, Master Thesis, 60 ECTS, University of Applied Sciences Aachen, Aachen, Germany, 2016
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  5. Glock, P.: Design and Evaluation of an SVM Framework for Scientific Data Applications, Master Thesis, 60 ECTS, University of Maastricht, Maastricht, The Netherlands, 2015
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  6. Klauck, S.: Task Core Mappings – Optimized MPI Process Placement for Blue Gene/Q Systems, Master Thesis, 60 ECTS, University of Potsdam, Hasso Plattner Institute, IT Systems Engineering, Potsdam, Germany, 2014
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  7. Holl, S.: Eclipse-based Client Support for Scientific Biological Applications in e-Science Infrastructures, Master Thesis, 60 ECTS, University of Duesseldorf, Institute of Informatics, Duesseldorf, Germany, 2008
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Doctoral Thesis

  1. Goetz, M.: Scalable Data Analysis in High Performance Computing, Doctoral Thesis, University of Iceland, School of Engineering and Natural Sciences (SENS), Reykjavik, Iceland, 2017
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Member of Doctoral Board

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