Welcome

• Robert Feidenhans'l (European XFEL)

Classification of diffraction patterns in single particle imaging experiments using a convolutional neural network

• Alexandr Ignatenko (Deutsches Elektronen-Synchrotron DESY)

Improving the quality of the spectroscopic analysis by supervised machine learning

• Alexander Guda ( Southern Federal University)

ML applications within automated data analysis pipelines for MX at Diamond - current state and future plans

• Melanie Vollmar (Diamond Light Source)

Machine Learning for data reduction in serial x-ray diffraction imaging

• Alireza Sadri (Center for Free-Electron Laser Science (DESY))

Improving BESSY II Operation with ML Tools: RL for Beam Control

• Luis Vera Ramirez (Helmholtz-Zentrum Berlin)

Reinforcement Learning parameter optimization of classical peak-finding and indexing methods

• Danilo Enoque Ferreira de Lima (European XFEL)
• Arman Davtyan (European XFEL)

ML-based sample centering for macromolecular x-ray crystallography with MXAimbot

• Isak Lindhé (MAX IV)

Finding similarity in diffraction images using deep learning

• Julian Zimmermann (ETH Zürich)

Applied Deep Learning for Segmentation and Object Detection at DESY

• Philipp Heuser (Deutsches Elektronen-Synchrotron DESY)

ML-driven Reconstruction of X-ray Scattering Data

• Nico Hoffmann (Helmholtz-Zentrum Dresden-Rossendorf)

Application of neural networks to small angle scattering data analysis

• Dima Molodenskiy (European Molecular Biology Laboratory Hamburg)

Disentangling structural heterogeneity in X-ray single particle imaging

• Kartik Ayyer (Max Planck Institute for the Structure and Dynamics of Matter)

Phase retrieval via deep neural networks

• Longlong Wu (Brookhaven National Lab)

Recovering the Phase and Amplitude of X-ray FEL Pulses with Neural Networks

• Daniel Fried Ratner (Stanford Linear Accelerator Center)

Summary

• Luca Gelisio (European XFEL)