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

List of speakers

Mark Basham


Machine learning to accelerate materials discovery, modeling, and experiment at DIAMOND

Keith Butler


Machine learning accelerated analysis of materials data: The Smart facility

Jacqui Cole

University of Cambridge/ISIS

Machine Learning at ISIS

Vincent Favre-Nicolin


Machine Learning needs at ESRF

Daniel Franke

EMBL Hamburg

Machine learning applications for Small Angle X-ray Scattering data collection and analysis at EMBL-Hamburg

Garrett Granroth


Machine Learning for accelerating understanding from Neutron Scattering Data

Sergei Grudinin


What does artificial intelligence see in 3D protein structures?

Allard Hendriksen CWI Machine Learning for improving image resolution in tomography

Jeyan Thiyagalingam 


Scientific Machine Learning Benchmarks

Christoph Koch

HU Berlin

Applications of Artificial Neural Networks in Electron Microscopy

Paolo Mutti


Machine Learning at ILL

Daniel Ratner SLAC Machine learning for an XFEL accelerator

Joel Saltz

Stony Brook University

Deriving the big picture from huge spatial datasets: How to make a little training data go a long way

James Sethian

LBNL/UC Berkeley

DOE's Center for Advanced Mathematics for Energy Research Applications (CAMERA): Artificial Intelligence, Machine Learning, and Experimental Facilities: Present and Future

Carlos Oscar S. Sorzano

CNB Madrid

Machine learning algorithms for image processing in CryoEM

Bill Triggs Laboratoire Jean Kuntzmann Introduction to Machine Learning and Deep Neural Networks for scattering science

Michael Unser

EPFL, Lausanne

Biomedical image reconstruction: From the foundations to deep neural networks

Sofia Vallecorsa


Deep Generative Models for detector simulation

Stefan Wild


Machine Learning at Argonne National Lab


The list of speakers is regularly updated.