Prof. Dr. - Ing. Morris Riedel

Morris Riedel Publications

Publications

Publications – Overview

Theses Candidate’s or Master’s thesis Riedel, M.: Design and Evaluation of a Collaborative Online Visualization and Steering Framework for e-Science Application in Grids, Master Thesis (2007), University of Hagen, Berichte des Forschungszentrum Juelichs, 2007 [ JUSER ] Riedel, M.: Prospects and Realization of Flexible Service Offers in a Grid Environment, Diploma Thesis (2004), University of Applied Sciences Cologne, Berichte des Forschungszentrum Juelichs, 2004 (Note: 6-month diploma theses are the German pre-Bologna equivalent of master’s theses) [ JUSER ] Doctoral thesis Riedel, M.: Design and Applications of an Interoperability Reference Model for Production e-Science Infrastructures, PhD Thesis (2012), Karlsruhe Institute of...

Morris Riedel Teaching 0

Teaching

Teaching – Overview

Teaching Experience Adjunct Lecturer, Lecturer, Senior Lecturer or Professor University Course: High Performance Computing – Advanced Scientific Computing, REI105M, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2019 [ MORE ] 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 [ MORE ] University Course: High Performance Computing – Advanced Scientific Computing, REI105M, School of Engineering and Natural Sciences, University of Iceland, Iceland, Fall 2017 [ MORE ] University Course: Cloud Computing and Big Data – Internet-based...

Morris Riedel Service 0

Service

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 The 21st IEEE International Conference on High Performance Computing and Communications (HPCC-2019), August 10-12, 2019, Zhangjiajie, China [ 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...

Morris Riedel Research 0

Research

Research – Overview

Research Areas Research of the high productivity data processing research group at Forschungszentrum Juelich – Juelich Supercomputing Centre and University of Iceland focuses on application-driven parallel and scalable machine learning methods including related areas such as feature engineering, statistical data mining, and innovative deep learning techniques. In our projects we are cooperating with academic research groups worldside as well as with selected industry companies including the support of start-ups. Additional funding sources are the European Union or the German Federal Ministry of Education and Research. If you are interested in one of these research topics please contact me by email...

Morris Riedel Theses 0

Theses

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

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

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 of Forschungszentrum Juelich 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)...

2019 HPC Course Fall 2019 Course Outline 0

Teaching

HPC – Course Fall 2019

High Performance Computing – Course Fall 2019 Advanced Scientific Computing 16 university lectures with additional practical lectures for hands-on exercises in context University of Iceland, School of Engineering and Natural Sciences Faculty of Industrial Engineering, Mechanical Engineering and Computer Science Fall 2019 Lecture 0 – Prologue Slides PDF (9,16 MB) Fully packed room teaching Lecture 0 – Prologue of our High Performance Computing course with ~50 students of @Haskoli_Islands @uni_iceland including Modular Supercomputing by @DEEPprojects @fzj_jsc @fz_juelich @helmholtz_de @uisens @helmholtz_ai slides: https://t.co/kyb7Sq31LX pic.twitter.com/VztJLeAFpH — Morris Riedel (@MorrisRiedel) August 27, 2019 Practical Lecture 0.1 – Short Introduction to UNIX & SSH...

2019-08-23 Neural Architecture Search With Reinforcement Learning Morris Riedel 0

Talks

Neural Architecture Search with Reinforcement Learning

Neural Architecture Search with Reinforcement Learning Invited Talk 5th International Summer School on Big Data and Machine Learning Technical University of Dresden, Dresden, Germany 2019-08-23 [ Slides ~18.8 MB (pdf) ] [ Event ] Invited talk on #neuralarchitecturesearch & #ReinforcementLearning @ Technical University of Dresden @zih_tud @Sca_DS presenting modular supercomputing & #AI by @fz_juelich @fzj_jsc @Haskoli_Islands @uni_iceland @uisens @DEEPprojects @helmholtz_ai slides: https://t.co/XFKX07tWOm pic.twitter.com/kQXGZkuY7T — Morris Riedel (@MorrisRiedel) August 23, 2019 Sieh dir diesen Beitrag auf Instagram an Invited talk on #neuralarchitecturesearch & #ReinforcementLearning @ Technical University of Dresden @tudresden ZIH @Sca_DS presenting modular supercomputing & #AI by @forschungszentrum_juelich #julichsupercomputingcenter @haskoli_islands...

2019-08-21 Machine Learning Co-Design of Future HPC Systems Morris Riedel 0

Talks

Machine Learning Co-Design of Future HPC Systems

Machine Learning Co-Design of Future HPC Systems BigBrain Workshop 2019 Seminar with Topic: Artificial Intelligence (AI) Methods and HPC Based Processing Juelich Supercomputing Centre, Forschungszentrum Juelich, Juelich, Germany 2019-08-21 [ Slides ~18.1 MB (pdf) ] Morris Riedel on the complex relationship between Deep Learning, Big Data and Modular Supercomputing at the Big Brain Workshop @HBPHighPerfComp @hbp @fz_juelich @fzj_jsc @Haskoli_Islands @uni_iceland @uisens @DEEPprojects @helmholtz_ai – https://t.co/2ynAPPBtBw… pic.twitter.com/bA3axk73jq — Thomas Lippert (@Tomtherhymer) August 21, 2019 Presenting with Thomas Lippert at our BigBrain Workshop @BigBrainProject yesterday about #AI methods, #HPC in #neuroscience & modular supercomputing co-design by @fz_juelich @fzj_jsc @Haskoli_Islands @uni_iceland @uisens @DEEPprojects...

Morris Riedel Neural Architecture Search 0

Research

Neural Architecture Search

Neural Architecture Search Succesful Examples Succesful NAS example in image classification: Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le. Learning transferable, architectures for scalable image recognition. In Conference on Computer Vision and Pattern Recognition, 2018. Succesful NAS example in image classification: Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le. Aging Evolution for Image Classifier Architecture Search. In AAAI, 2019. Succesful NAS example in object detection: Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le. Learning transferable, architectures for scalable image recognition. In Conference on Computer Vision and Pattern Recognition, 2018. Succesful NAS example in semantic...

2019-08-19 Importance of High Performance Computing for Machine Learning Morris Riedel 0

Talks

Importance of High Performance Computing for Machine Learning

Importance of High Performance Computing for Machine Learning Visiting Program for Delegation from South Korea organized by NRW.INVEST Seminar with Topic: Global Research and Development Incubating Center Juelich Supercomputing Centre, Forschungszentrum Juelich, Juelich, Germany 2019-08-19 [ Slides ~18.1 MB (pdf) ]

Morris Riedel Graphics Processing Unit 0

Research

Graphics Processing Card

Graphics Processing Card Research Activities of using GPUDirect in a Modular Supercomputing Architecture (MSA) during the EU DEEP-EST project. Social Media Hands-on GPU training with my PhD student Rocco Sedona @Rocco_Hashtag in a nice course by Andreas Heerten @AndiH @fz_juelich @fzj_jsc via CUDA on JUWELS & comparing architectures Kepler/Pascal/Volta @Haskoli_Islands @uni_iceland @uisens @DEEPprojects see: https://t.co/Jxz71NMpfn pic.twitter.com/NWeJbZ5s2R — Morris Riedel (@MorrisRiedel) August 8, 2019 Sieh dir diesen Beitrag auf Instagram an Hands-on GPU training with my PhD student Rocco Sedona @rocco_hashtag in a nice course by Andreas Heerten @andiherten at @forschungszentrum_juelich #julichsupercomputingcenter via CUDA on JUWELS & comparing architectures Kepler/Pascal/Volta @haskoli_islands...

Cloud Deep Networks for Hyperspectral Image Analysis 0

Publications

Cloud Deep Networks for Hyperspectral Image Analysis

Cloud Deep Networks for Hyperspectral Image Analysis Haut, J.M., Gallardo, J.A., Paoletti, M.E., Cavallaro, G., Plaza, J., Plaza, A., Riedel, M. IEEE Transactions on Geoscience and Remote Sensing, PP(99):1-17, 2019 [ JOURNAL ] [ DOI ] [ GOOGLE SCHOLAR ] [ RESEARCHGATE ] Abstract: Advances in remote sensing hardware have led to a significantly increased capability for high-quality data acquisition, which allows the collection of remotely sensed images with very high spatial, spectral, and radiometric resolution. This trend calls for the development of new techniques to enhance the way that such unprecedented volumes of data are stored, processed, and analyzed....