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

List of speakers

Mark Basham

DLS

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

Keith Butler

SCD, RAL

Machine learning accelerated analysis of materials data: The Smart facility

Jacqui Cole

University of Cambridge/ISIS

Machine Learning at ISIS

Vincent Favre-Nicolin

ESRF

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

Sergei Grudinin

Inria/CNRS

What does artificial intelligence see in 3D protein structures?

Allard Hendriksen CWI Machine Learning for improving image resolution in tomography

Tony Hey

SCD,RAL

Scientific Machine Learning Benchmarks

Christoph Koch

HU Berlin

Applications of Artificial Neural Networks in Electron Microscopy

Paolo Mutti

ILL

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

Jamie Sethian

LBNL / DOE Labs

The CAMERA project at Berkeley National Lab

Carlos Oscar S. Sorzano

CNB Madrid

Machine learning algorithms for image processing in CryoEM

Alan Tennant

ORNL

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

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

Michael Unser

University of Zurich

Biomedical image reconstruction: From the foundations to deep neural networks

Sofia Vallecorsa

CERN

Deep Generative Models for detector simulation

Stefan Wild

ANL

Machine Learning at Argonne National Lab

 

The list of speakers is regularly updated.

 

 

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