Login

Artificial Intelligence Applied to Photon and Neutron Science

12–14 Nov 2019
Europe/Paris timezone
- Max number of participants reached, further registrations will be on waiting list -

Contact

Session

Afternoon 1

12 Nov 2019, 13:40

Presentation materials

There are no materials yet.

  1. 125. Welcome from ESRF Director
    Dr Francesco Sette
    12/11/2019, 13:40
  2. 126. Welcome from ILL Director
    Prof. Mark Johnson
    12/11/2019, 13:50
  3. 39. Introduction to Machine Learning and Deep Neural Networks for scattering science
    Dr Bill Triggs (LJK Grenoble)
    12/11/2019, 14:00

    I will give a brief introduction to modern machine learning and deep learning techniques aimed at researchers planning to use them for X-ray and neutron scattering applications. Areas covered will include basic ML terminology and concerns, a quick tour of some probabilistic methods including Gaussian Processes (Kriging), and a discussion of modern neural methods including deep nets,...

    Go to contribution page
  4. 40. Improving data analysis using artificial intelligence: an ESRF perspective
    Dr Vincent Favre-Nicolin (ESRF)
    12/11/2019, 14:50

    The ESRF will soon restart after its Extremely Brilliant Source upgrade, which will provide two orders of magnitude improved photon flux for many experiments, and will also come with several new beamlines producing high-throughput data, from macromolecular crystallography to large volume tomography. This creates many challenges in terms of data handling, both from the point of view of the...

    Go to contribution page
  5. 41. Machine learning applications for Small Angle X-ray Scattering data collection and analysis at EMBL-Hamburg
    Dr Daniel Franke (EMBL)
    12/11/2019, 15:15

    In recent years, machine learning and artificial intelligence rapidly gained popularity in many fields of industry and research, particularly as a tool capable of extracting information from amounts of data often too large to analyze manually. Small Angle X-Ray Scattering (SAXS) of biological macromolecules in solution is routinely being used to evaluate the structural parameters and low...

    Go to contribution page
  6. 74. Machine learning for an XFEL accelerator
    Dr Daniel Ratner (SLAC)
    13/11/2019, 14:00

    X-ray Free Electron Lasers (XFELs) are among the most complex modern accelerator facilities. With large parameter spaces, highly non-linear behavior, and large data rates, there are expanding opportunities to apply machine learning to XFEL operation and design. In this talk I will give an overview of the challenges, and will cover several applications of machine learning, including online...

    Go to contribution page
  7. 75. Machine Learning at Argonne National Lab
    Dr Stefan Wild (ANL)
    13/11/2019, 14:50

    We overview artificial intelligence and machine learning for science activities at Argonne National Laboratory. We particularly emphasize cross-laboratory efforts involving the Advanced Photon Source. These include advances in the confluence of high-performance computing and photon sciences; large-scale reconstruction; new algorithms for automating error-correction and experimental design; and...

    Go to contribution page
  8. 76. Scientific Machine Learning Benchmarks
    Dr Jeyan Thiyagalingam (SCD RAL)
    13/11/2019, 15:15

    The use of artificial intelligence (AI) technologies, and of deep learning neural networks in particular, is already having a major impact on many aspects of our lives. The challenge for scientists is to explore how these technologies could have a similar impact for scientific discovery. Already Google DeepMind’s AlphaFold tool has achieved some impressive results for protein folding...

    Go to contribution page
Building timetable...