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GridKa School 2019 - The Art of Data

Aug 26 – 30, 2019
KIT, Campus North, FTU
Europe/Berlin timezone

Contact

Session

Plenary

Aug 26, 2019, 2:00 PM
Aula (FTU)

Aula

FTU

Conveners

Plenary

  • René Caspart (KIT - Karlsruhe Institute of Technology (DE))

Plenary

  • Max Fischer (Karlsruhe Institute of Technology)

Plenary

  • Andreas Heiss (KIT)

Plenary

  • Manuel Giffels (KIT)

Plenary

  • Eileen Kühn (Karlsruhe Institute of Technology)

Presentation materials

There are no materials yet.

  1. 18. Welcome to KIT
    Achim Streit (KIT-SCC)
    8/26/19, 2:00 PM
    Plenary Talk
  2. 17. Welcome to GridKa School
    René Caspart (KIT - Karlsruhe Institute of Technology (DE))
    8/26/19, 2:30 PM
    Plenary Talk
  3. 39. Evaluating classifier performance
    Prof. Tilmann Gneiting (Evaluating classifier performance)
    8/26/19, 2:50 PM
    Plenary Talk
  4. 31. Advancing Openness and Transparency in Scientific Contributions
    Rima-Maria Rahal (Tilburg University)
    8/26/19, 4:00 PM
    Plenary Talk
  5. 32. Research Data Management: The Serviceteam RDM@KIT
    Dr Kerstin Wedlich-Zachodin (KIT), Ulrich Robert (KIT)
    8/26/19, 4:40 PM
    Plenary Talk
  6. 33. Responsible Research and Technology Assessment
    Dr Julia Hahn (Karlsruhe Institute of Technology)
    8/26/19, 5:20 PM
    Plenary Talk
  7. 11. Why Rust?
    Mr Oliver Scherer (KIT - Karlsruhe Institut of Technology)
    8/27/19, 9:00 AM
    Plenary Talk
  8. 40. Kubernetes meets Data Scientists - An Experience Report
    Prof. Peter Tröger (Beuth Hochschule für Technik Berlin)
    8/27/19, 9:40 AM
    Plenary Talk
  9. 45. News
    René Caspart (KIT - Karlsruhe Institute of Technology (DE))
    8/27/19, 10:20 AM
  10. 24. Make Your Data FABulous
    Philipp Krenn (Elastic)
    8/27/19, 10:40 AM
    Plenary Talk

    The CAP theorem is widely known for distributed systems, but it's not the only tradeoff you should be aware of. For datastores there is also the FAB theory and just like with the CAP theorem you can only pick two:
    * Fast: Results are real-time or near real-time instead of batch oriented.
    * Accurate: Answers are exact and don't have a margin of error.
    * Big: You require horizontal scaling and...

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  11. 20. Quantum Computing and IBM Q: An Introduction
    Dr Oliver Oberst (IBM)
    8/27/19, 11:20 AM
    Plenary Talk
  12. 36. Challenges and plans of the SKA Regional Centres
    Zheng Meyer-Zhao
    8/28/19, 9:00 AM
    Plenary Talk
  13. 37. Thrill: High-Performance Algorithmic Distributed Batch Data Processing with C++
    Timo Bingmann (KIT)
    8/28/19, 9:40 AM
    Plenary Talk
  14. 34. Scalable Graph Analytics
    Kevin Gomez (Uni Leipzig)
    8/28/19, 10:40 AM
    Plenary Talk
  15. 23. Compute Aspects of Machine Learning
    Prof. Janis Keuper (Hochschule Offenburg)
    8/28/19, 11:20 AM
    Plenary Talk
  16. 44. Design for High-Performance Computing Applications
    Dr Marco Berghoff (KIT)
    8/29/19, 9:00 AM
    Plenary Talk
  17. 14. GPU Programming 101
    Andreas Herten (FZ Jülich)
    8/29/19, 9:40 AM
    Plenary Talk
  18. 15. Intel’s Hardware and Software Solutions and Directions for Artificial Intelligence
    Edmund Preiss (Intel)
    8/29/19, 11:00 AM
    Plenary Talk
  19. 35. DevOps for Machine Learning in Academia
    Valentin Kozlov (Karlsruhe Institute of Technology)
    8/30/19, 9:00 AM
    Plenary Talk
  20. 41. Continuous Benchmarking
    Dr Hartwig Anzt (Karlsruher Institut für Technologie)
    8/30/19, 9:40 AM
    Plenary Talk
  21. 42. Modern Classification Tasks in LHC Physics
    Tilman Plehn (Heidelberg University)
    8/30/19, 10:40 AM
    Plenary Talk
  22. 19. Conclusions
    René Caspart (KIT - Karlsruhe Institute of Technology (DE))
    8/30/19, 11:20 AM
    Plenary Talk
  23. 38. Preservation
    Jens-Martin Löbel
    Plenary Talk
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