August 11 and 13
AI/ML for
ALS Research Innovation Forum
a virtual event at Berkeley Lab
a virtual event at Berkeley Lab
hosted by the Advanced Light Source
hosted by the Advanced Light Source
A forum on the development of new
A forum on the development of new
experimental and computational approaches
experimental and computational approaches
in a synchrotron environment
in a synchrotron environment
At the ALS we, as members of the Instrumentation-Thrust Area, are in a unique position to lead the development of new experimental and computational approaches in a synchrotron environment. Recent breakthroughs in machine learning have had a tremendous impact across industry and science domains, largely due to the readily available large labeled data sets and new developments in more effective network architectures.
At the ALS we, as members of the Instrumentation-Thrust Area, are in a unique position to lead the development of new experimental and computational approaches in a synchrotron environment. Recent breakthroughs in machine learning have had a tremendous impact across industry and science domains, largely due to the readily available large labeled data sets and new developments in more effective network architectures.
However, there are several challenges associated with capitalizing on these ideas in the context of scientific machine learning. Even if the data sets created at the ALS are by their nature large (e.g. 4D STXM and ptychography, nanoARPES, XPCS, ... ), they usually only represent a subset of materials and parameter space. Extrapolation from there to other materials and properties can be very challenging.
However, there are several challenges associated with capitalizing on these ideas in the context of scientific machine learning. Even if the data sets created at the ALS are by their nature large (e.g. 4D STXM and ptychography, nanoARPES, XPCS, ... ), they usually only represent a subset of materials and parameter space. Extrapolation from there to other materials and properties can be very challenging.
The combination of the ALS, the Molecular Foundry, NERSC, and our collaborating colleagues at CAMERA, MSD, CSD, UCB, and all other academic and industrial research activities of the Bay Area provide a unique environment to tackle this and other challenges of scientific machine learning.
The combination of the ALS, the Molecular Foundry, NERSC, and our collaborating colleagues at CAMERA, MSD, CSD, UCB, and all other academic and industrial research activities of the Bay Area provide a unique environment to tackle this and other challenges of scientific machine learning.
Speakers
Speakers
Nathan Melton
Nathan Melton
ALS Postdoctoral Scholar, LBNL
Tess Smidt
Tess Smidt
Luis W. Alvarez Postdoctoral Fellow, LBNL
Dani Ushizima
Dani Ushizima
Staff Scientist, LBNL
Affiliate Faculty, UCSF
Mathew Cherukara
Mathew Cherukara
Assistant Scientist, Advanced Photon Source , ANL
Marcus Noack
Marcus Noack
CAMERA Postdoctoral Fellow, LBNL
Sergei Kalinin
Sergei Kalinin
Distinguished corporate fellow, Center For Nanophase Materials Sciences, ORNL
Steve Whitelam
Steve Whitelam
Staff Scientist, Molecular Foundry, LBNL
Subramanian Sankaranarayanan
Subramanian Sankaranarayanan
Theory and Modeling Group Leader, Center for Nanoscale Materials, ANL
Read the most recent AI-ML DOE report below
Read the most recent AI-ML DOE report below
AI-ML_Report.pdfLet us know if you'll be attending!
Let us know if you'll be attending!
AI-ML_Companion_Document.pdf