Morris Riedel Talks

Talks – Overview

Lectures Plenary Lecture or Keynote Address at an International Academic Conference Scientific Big Data Analytics – Practice & Experience, Invited Keynote, The International Conference on Cloud and Autonomic Computing (CAC 2014), September 8 – 12, 2014, Imperial College, London, UK Public Guest Lecture at a University outside of Iceland Selected Parallel and Scalable Methods for Scientific Big Data Analytics, Invited Talk, ZIH-Kolloquium, May 21, 2015, Technical University of Dresden, Germany Enabling Parallel and Scalable Tools for Scientific Big Data Analytics, Invited Talk, AixCAPE Spring Meeting 2015, May 6, 2015, RWTH Technical University of Aachen, Germany Bedeutung von Interoperabilität und Standards...

Morris Riedel Teaching 0

Teaching – Overview

Teaching Experience Adjunct Lecturer, Lecturer, Senior Lecturer or Professor 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 [ MORE ] University Course: High Performance Computing – Advanced Scientific Computing, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2017 [ MORE ] 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 University Course: High Performance Computing B – High Productivity Processing...

Morris Riedel Publications

Publications – Overview

Book Chapters Peer-reviewed Publications by the World’s most respected Academic Publishers Riedel, M.: e-Science Infrastructure Interoperability Guide: The Seven Steps Toward Interoperability for e-Science, Springer Book Series Computer Communications and Networks, Guide to e-Science – Next Generation Scientific Research and Discovery, 2011, pp. 233-264, ISBN 978-0-85729-439-5 [ BOOK SERIES ] [ DOI ] [ JUSER ] [ RESEARCHGATE ] Streit, A., Erwin, D., Lippert, T., Mallmann, D., Menday, R., Rambadt, M., Riedel, M., Romberg, M., Schuller, B., Wieder, P.: UNICORE – From Project Results to Production Grids, Elsevier Book Series Advances in Parallel Computing, Vol 14, 2005, pp. 357-376, ISBN...

Morris Riedel Service 0

Service – Overview

Organisation of International Scientific Conferences Topic Chair Data Management, Analytics and Deep Learning 25th Euro-Par Conference, August 26-30, 2019, Goettingen, Germany [ Web Page ] Program Committee Member 8th IEEE International Conference on Big Data and Cloud Computing (BDCloud 2018), December 11-13, 2018, Melbourne, Australia [ Web Page ] Program Committee Member 9th Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE 2018), held in conjunction with the 14th IEEE International Conference on e-Science (e-Science 2018), October 29 – November 1st, 2018, Amsterdam, the Netherlands [ Web Page ] Program Committee Member 14th International Workshop on Scheduling and Resource...

Morris Riedel Theses 0

Theses – Overview

Theses Proposals I’m always open to proposals for bachelor, master, or doctoral thesis topics that are along the lines of my research interests. Feel free to contact me for details. Doctoral Theses [In Progress] Deep Learning Application Co-Design on Innovative HPC Architectures Ernir Erlingsson, Doctoral Thesis University of Iceland, School of Engineering and Natural Sciences (SENS), Iceland Phd student @ernire Ernir Erlingsson of @Haskoli_Islands alongside other data-driven & HPC application specialists explain technology experts at the @DEEPprojects F2F meeting how to take advantage of the modular supercomputing concept implemented by @fzj_jsc pic.twitter.com/aby8UE95l9 — Morris Riedel (@MorrisRiedel) May 8, 2018 Today...

Morris Riedel Media 0

Media – Overview

2017 Invited Tutorial – Introduction to Machine Learning Algorithms Morris Riedel Ghent University, six lectures including exercises 2017-11-23 – 2017-11-24 [ Event ] Lecture 1 – Machine Learning Fundamentals Lecture 2 – Unsupervised Clustering and Applications Lecture 3 – Supervised Classification and Applications Lecture 4 – Classification Challenges and Solutions Lecture 5 – Regularization and Support Vector Machines Lecture 6 – Validation and Parallelization Benefits

Morris Riedel Biography 0

Biography

Biography Executive Summary Prof. Dr. – Ing. Morris Riedel is an Adjunct Associate Professor at the School of Engineering and Natural Sciences of the University of Iceland. He received his PhD from the Karlsruhe Institute of Technology (KIT) and works in parallel and distributed systems since 15 years. He previously held various positions at the Juelich Supercomputing Centre in Germany. At this institute, he is also the head of a specific scientific research group focused on ‘High Productivity Data Processing’ and a cross-sectional team ‘Deep Learning’. Professional Networks and Associations Institute of Electrical and Electronics Engineers (IEEE) is the world’s...

Morris Riedel DBSCAN 0

DBSCAN

DBSCAN Parallel & Scalable DBSCAN Goetz, M., Bodenstein, C., Riedel, M.: HPDBSCAN – Highly Parallel DBSCAN, in conference proceedings of ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2015), Machine Learning in HPC Environments (MLHPC 2015) Workshop, November 15-20, 2015, Austin, Texas, USA [ EVENT ] [ DOI ] [ JUSER ] [ RESEARCHGATE ] [ MORE ] DBSCAN Applications Bodenstein, C., Goetz, M., Jansen, A., Scholz, H., Riedel, M.: Automatic Object Detection using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay, in conference proceedings of the 15th IEEE International Conference on Machine Learning and...

Deutscher Bundestag Impulsvortrag Kuenstliche Intelligenz Morris Riedel 0

Deutscher Bundestag Impulsvortrag Kuenstliche Intelligenz

Deutscher Bundestag Impulsvortrag Kuenstliche Intelligenz Deutscher Bundestag, Enquete-Kommission ‘Kuenstliche Intelligenz’ Berlin, Germany 2019-03-11 [ Slides ~4.86 MB (pdf) ] [ EVENT ] [ PRESS ] Video of my talk @ Deutscher Bundestag German federal parliament now at https://t.co/dcqT15JYPY discussing among #ArtificialIntelligence experts HAICU @helmholtz_en SMITH, ON4OFF & Modular Supercomputing by @DEEPprojects @fzj_jsc @fz_juelich @uisens @uni_iceland @Haskoli_Islands pic.twitter.com/YSzWHtpmru — Morris Riedel (@MorrisRiedel) March 21, 2019 Invited talk at Deutscher Bundestag – the German federal parliament – presenting artificial intelligence chances with examples of SMITH, ON4OFF & Modular Supercomputing by @DEEPprojects @fzj_jsc @fz_juelich @uisens @uni_iceland @Haskoli_Islands – talk at: https://t.co/xCDitaxS71 pic.twitter.com/S5VkSRacRQ —...

Morris Riedel Retail Analytics 0

Retail Analytics

Retail Analytics Kuenstliche Intelligenz Links (German Language) Strategie Künstliche Intelligenz der Bundesregierung, November 2018 [ MORE ] ON4OFF EU EFRE Project ON4OFF Team @PieperBeauty @paluno_se @adessoAG @INtelegenceGmbH @fz_juelich & ProXperts discuss with @ProfHeinemann using #ArtificialIntelligence in #Retail via #NLP & #ModularSupercomputing by @DEEPprojects @fzj_jsc @Haskoli_Islands @uisens @uni_iceland – https://t.co/xal4kRdWcy pic.twitter.com/kPIvhOQp5a — Morris Riedel (@MorrisRiedel) March 20, 2019 ON4OFF Project Team @paluno_se @adessoAG @INtelegenceGmbH @fz_juelich & ProXperts discuss with @ProfHeinemann selected #Retail use cases for @PieperBeauty using #MachineLearning & #DeepLearning with Modular Supercomputing driven by @fzj_jsc @Haskoli_Islands @uisens @uni_iceland pic.twitter.com/a5HtTTY4nb — Morris Riedel (@MorrisRiedel) March 7, 2019 ON4OFF Project Team @adessoAG...

2019-02-25 PRACE Tutorial Parallel and Scalable Machine Learning Morris Riedel 0

PRACE Tutorial – Parallel and Scalable Machine Learning

PRACE Tutorial: Parallel and Scalable Machine Learning Invited Tutorial PRACE Advanced Training Center, Juelich Supercomputing Centre, Germany 2019-02-25 – 2019-02-27 [ EVENT ] Lecture 1 – Parallel and Scalable Machine Learning driven by HPC (pdf, ~9,97 MB) Lecture 2 – Introduction to Machine Learning Fundamentals – Theory (pdf, ~12,9 MB) Lecture 3 – Introduction to Machine Learning Fundamentals – Practice (Jupyter Notebooks – request resources contacting m.riedel@morrisriedel.de) [Martin Schultz] Lecture 4 – Feed Forward Neural Networks (Jupyter Notebooks – request resources contacting m.riedel@morrisriedel.de) [Martin Schultz] Lecture 5 – Feed Forward Neural Networks (Jupyter Notebooks – request resources contacting m.riedel@morrisriedel.de) Lecture...

2019-02-18 SMITH ASIC Use Case Related Work VPH Morris Riedel 0

SMITH ASIC Use Case and Related Work Virtual Physiological Human

SMITH ASIC Use Case and Related Work Virtual Physiological Human SMITH ASIC AG2 Workshop RWTH Aachen University Clinic, Aachen, Germany 2019-02-18 [ Slides ~7.10 MB (pdf) ] SMITH Algorithmic Surveillance of Intensive Care Unit Patients (ASIC) workshop @UniklinikAachen discussing virtual patient models like @VPH_Institute using modular supercomputing of @DEEPprojects @fzj_jsc @fz_juelich @Haskoli_Islands @uisens @uni_iceland – https://t.co/xCDitaxS71 pic.twitter.com/3Cw2vKKBux — Morris Riedel (@MorrisRiedel) February 18, 2019

2019-02-15 ON4OFF Projekt Juelich Supercomputing Centre Rolle und Ziele Morris Riedel 0

ON4OFF Projekt – Juelich Supercomputing Centre – Rolle und Ziele

ON4OFF Projekt – Juelich Supercomputing Centre – Rolle und Ziele ON4OFF Vision Workshop Adesso, Essen Germany 2019-02-15 [ Slides ~1.69 MB (pdf) ] ON4OFF Project Team @adessoAG @INtelegenceGmbH @paluno_se @PieperBeauty @fz_juelich explores with @ProfHeinemann how to improve retail using also #MachineLearning & modular supercomputing by @DEEPprojects @fzj_jsc @uisens @Haskoli_Islands @uni_iceland – https://t.co/xCDitaxS71 pic.twitter.com/urAlzECiAx — Morris Riedel (@MorrisRiedel) February 16, 2019

2019-01-21 DEEP EST Tutorial Machine Learning and Modular Supercomputing Content 0

DEEP-EST Tutorial – Machine Learning and Modular Supercomputing

DEEP-EST Tutorial – Machine Learning and Modular Supercomputing HIPEAC Conference 2019 Conference Centre, Valencia, Spain 2019-01-21 [ Event ] Abstract The fast training of traditional machine learning models and more innovative deep learning networks from increasingly growing large quantities of scientific and engineering datasets (aka ‘Big Data‘) requires high performance computing (HPC) on modern supercomputers today. HPC technologies such as those developed within the European DEEP-EST project provide innovative approaches w.r.t. processing, memory, and modular supercomputing usage during training, testing, and validation processes. Materials [ Lecture 1: Modular Supercomputing and Machine Learning – Welcome to HiPEAC Tutorial – Slides ~1.12...

2019-01-16 Overview German HPC Resources and Facilities Helmholtz Morris Riedel 0

Overview of German HPC Resources and Facilities – Helmholtz

Overview of German HPC Resources and Facilities – Helmholtz CIFAR – Helmholtz Association Workshop – Artificial Intelligence for Neuroscience Mars Discovery District, Toronto, Canada 2019-01-16 [ Slides ~15.61 MB (pdf) ] [ Event ] Thanks to @CIFAR_News organizers for a great AI for Neuroscience event last week & crafting ideas with A. Evans to connect to CBRAIN & talking about German High Performance Computing resources in @helmholtz_en @helmholtz_de @fzj_jsc @fz_juelich – slides: https://t.co/xCDitaxS71 pic.twitter.com/5NDH1Yq0ex — Morris Riedel (@MorrisRiedel) January 20, 2019