Name: Artificial Intelligence Applied to Photon and Neutron Science
Start: 2019-11-12T09:00:00+01:00
End: 2019-11-14T14:00:00+01:00
Location: No location set

Artificial Intelligence Applied to Photon and Neutron Science

from Tuesday, 12 November 2019 (09:00) to Thursday, 14 November 2019 (14:00)

Monday, 11 November 2019
Tuesday, 12 November 2019

11:00 Registration and cocktail lunch ESRF: entrance hall
Registration and cocktail lunch ESRF: entrance hall
11:00 - 13:00
13:40 Chairman Dr. Rudolf Dimper
Chairman Dr. Rudolf Dimper
13:40 - 15:40
Contributions

13:40 Welcome from ESRF Director - Francesco Sette

13:50 Welcome from ILL Director - Mark Johnson

14:00 Introduction to Machine Learning and Deep Neural Networks for scattering science - Bill Triggs (LJK Grenoble)

14:50 Improving data analysis using artificial intelligence: an ESRF perspective - Vincent Favre-Nicolin (ESRF)

15:15 Machine learning applications for Small Angle X-ray Scattering data collection and analysis at EMBL-Hamburg - Daniel Franke (EMBL)
15:40

15:40 - 16:00
16:00 Chairman Dr. Tony Hey
Chairman Dr. Tony Hey
16:00 - 18:30
Contributions

16:00 DOE's Center for Advanced Mathematics for Energy Research Applications (CAMERA): Artificial Intelligence, Machine Learning, and Experimental Facilities: Present and Future - Jamie Sethian (LBNL/UC Berkeley)

16:50 Machine Learning at ILL - Paolo Mutti

17:15 Machine learning algorithms for image processing in CryoEM - Carlos Oscar Sorzano (CNB Madrid)

17:40 Machine learning and artificial intelligence in MX - Melanie Vollmar (Diamond Light Source)

18:05 Convolutional neural networks for DESY photon science - Philipp Heuser (DESY)
19:00

19:00 - 21:00
Wednesday, 13 November 2019
09:00 Chairman Dr. Paolo Mutti
Chairman Dr. Paolo Mutti
09:00 - 10:40
Contributions

09:00 Biomedical image reconstruction: From the foundations to deep neural networks - Michael Unser (EPFL)

09:50 Machine Learning for improving image quality in tomography - Allard Hendriksen (CWI)

10:15 What does artificial intelligence see in 3D protein structures? - Sergei Grudinin (Inria/CNRS)
10:40 Group Photo
Group Photo
10:40 - 11:00
11:00 Chairman Dr. Paolo Mutti
Chairman Dr. Paolo Mutti
11:00 - 12:40
Contributions

11:00 Data-driven Materials Discovery for Functional Applications - Jacqui Cole (University of Cambridge/ISIS)

11:25 Applications of Artificial Neural Networks in Electron Microscopy - Christoph Koch (HU Berlin)

11:50 Deep learning for classifying and sorting diffraction images - Julian Zimmermann (Max-Born-Institut für Nichtlineare Optik und Kurzzeitspektroskopie)

12:15 PyFitit: the software for quantitative analysis of XANES spectra using machine-learning algorithms - Alexander Guda (The Smart Materials Research Institute, Southern Federal University)
12:40

12:40 - 14:00
14:00 Chairman Dr. Andy Gotz
Chairman Dr. Andy Gotz
14:00 - 15:40
Contributions

14:00 Machine learning for an XFEL accelerator - Daniel Ratner (SLAC)

14:50 Machine Learning at Argonne National Lab - Stefan Wild (ANL)

15:15 Scientific Machine Learning Benchmarks - Jeyan Thiyagalingam (SCD RAL)
15:40

15:40 - 16:00
16:00 Chairman Dr. Andy Gotz
Chairman Dr. Andy Gotz
16:00 - 18:05
Contributions

16:00 Machine Learning for accelerating understanding from Neutron Scattering Data - Garrett E. Granroth (ORNL)

16:25 Machine Learning at Diamond Light Source: Past, Present and Future - Mark Basham (DLS)

16:50 Big Data Science Center at the Shanghai Synchrotron Radiation Facility - The first Superfacility Platform in China - Alessandro Sepe (SSRF)

17:15 Deep learning for small angle scattering under grazing incidence - Marina Ganeva (JCNS / MLZ)

17:40 Towards automated analysis for neutron reflectivity - Daniil Mironov (STFC)
18:30 Aperitif & Dinner
Aperitif & Dinner
18:30 - 21:30
Thursday, 14 November 2019
09:00 Chairman Dr. Miguel Gonzalez
Chairman Dr. Miguel Gonzalez
09:00 - 10:40
Contributions

09:00 Deriving the big picture from huge spatial datasets: How to make a little training data go a long way - Joel Saltz (Stony Brook University)

09:50 Deep Generative Models for detector simulation - Sofia Vallecorsa (CERN)

10:15 Deep learning for Synchrotron X-ray Imaging - Xiaogang Yang (DESY)
10:40

10:40 - 11:00
11:00 Chairman Dr. Miguel Gonzalez
Chairman Dr. Miguel Gonzalez
11:00 - 11:50
Contributions

11:00 Machine learning accelerated analysis of materials data: The Smart facility - Keith Butler (SCD RAL)

11:25 Machine Learning for Spectroscopy: From Spectra to Atomic Structures - Marius Retegan (ESRF)
11:50

11:50 - 12:40