NAPAC2022 - List of Authors (Edelen, A.L.)

Paper	Title	Page
MOPA55	Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry	164
	J.P. Edelen, D.T. Abell, D.L. Bruhwiler, S.J. Coleman, N.M. Cook, A. Diaw, J.A. Einstein-Curtis, C.C. Hall, M.C. Kilpatrick, B. Nash, I.V. Pogorelov RadiaSoft LLC, Boulder, Colorado, USA K.A. Brown BNL, Upton, New York, USA S. Calder ORNL RAD, Oak Ridge, Tennessee, USA A.L. Edelen, B.D. O’Shea, R.J. Roussel SLAC, Menlo Park, California, USA C.M. Hoffmann ORNL, Oak Ridge, Tennessee, USA E.-C. Huang LANL, Los Alamos, New Mexico, USA P. Piot Northern Illinois University, DeKalb, Illinois, USA C. Tennant JLab, Newport News, Virginia, USA
	It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data-science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA55
About •	Received ※ 10 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 22 August 2022 — Issue date ※ 01 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUXE1	The Importance of Data, High-Performance Computing, and Artificial Intelligence/Machine Learning
	C.M. Sweeney LANL, Los Alamos, New Mexico, USA A.L. Edelen SLAC, Menlo Park, California, USA D.E. Martin ANL, Lemont, Illinois, USA
	As existing accelerator facilities are upgraded and new facilities come online, data volumes and velocity are increasing even with shorter data collection times. High-performance computing (HPC) systems doing simulation, data analytics and artificial intelligence/machine learning (AI/ML) are playing a major role in pre-experiment planning, design of experiments, real-time beam line and experiment analysis and control, and post-run data processing. Simulation and AI incorporated into experimental data analysis workflows are making efficient use of expensive facilities and accelerating scientific discoveries. HPC is experiencing its own growth, with exascale computers and AI acceleration coming online at several supercomputer centers. AI/ML is in the midst of rapid growth of techniques and expansion into new application areas. This session will focus on current and emerging technologies in HPC, experimental workflows, and AI/ML techniques to help you incorporate them into your own research. Dr. D. Martin will provide "HPC Overview" followed by "Workflows" by Dr. C. Sweeney. "AI and ML" by Dr. A. Edelen will be followed by community discussions and questions from the audience.
	Slides TUXE1 [11.946 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUXE3	Artificial Intelligence and Machine Learning for Particle Accelerators
	A.L. Edelen SLAC, Menlo Park, California, USA
	As existing accelerator facilities are upgraded and new facilities come online, data volumes and velocity are increasing even with shorter data collection times. High-performance computing (HPC) systems doing simulation, data analytics and artificial intelligence/machine learning (AI/ML) are playing a major role in pre-experiment planning, design of experiments, real-time beam line and experiment analysis and control, and post-run data processing. Simulation and AI incorporated into experimental data analysis workflows are making efficient use of expensive facilities and accelerating scientific discoveries. HPC is experiencing its own growth, with exascale computers and AI acceleration coming online at several supercomputer centers. AI/ML is in the midst of rapid growth of techniques and expansion into new application areas. This session will focus on current and emerging technologies in HPC, experimental workflows, and AI/ML techniques to help you incorporate them into your own research. Dr. D. Martin will provide "HPC Overview" followed by "Workflows" by Dr. C. Sweeney. "AI and ML" by Dr. A. Edelen will be followed by community discussions and questions from the audience.
	Slides TUXE3 [17.252 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry

164

J.P. Edelen, D.T. Abell, D.L. Bruhwiler, S.J. Coleman, N.M. Cook, A. Diaw, J.A. Einstein-Curtis, C.C. Hall, M.C. Kilpatrick, B. Nash, I.V. Pogorelov
RadiaSoft LLC, Boulder, Colorado, USA
K.A. Brown
BNL, Upton, New York, USA
S. Calder
ORNL RAD, Oak Ridge, Tennessee, USA
A.L. Edelen, B.D. O’Shea, R.J. Roussel
SLAC, Menlo Park, California, USA
C.M. Hoffmann
ORNL, Oak Ridge, Tennessee, USA
E.-C. Huang
LANL, Los Alamos, New Mexico, USA
P. Piot
Northern Illinois University, DeKalb, Illinois, USA
C. Tennant
JLab, Newport News, Virginia, USA

It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data-science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA55

About •

Received ※ 10 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 22 August 2022 — Issue date ※ 01 September 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUXE1

The Importance of Data, High-Performance Computing, and Artificial Intelligence/Machine Learning

C.M. Sweeney
LANL, Los Alamos, New Mexico, USA
A.L. Edelen
SLAC, Menlo Park, California, USA
D.E. Martin
ANL, Lemont, Illinois, USA

As existing accelerator facilities are upgraded and new facilities come online, data volumes and velocity are increasing even with shorter data collection times. High-performance computing (HPC) systems doing simulation, data analytics and artificial intelligence/machine learning (AI/ML) are playing a major role in pre-experiment planning, design of experiments, real-time beam line and experiment analysis and control, and post-run data processing. Simulation and AI incorporated into experimental data analysis workflows are making efficient use of expensive facilities and accelerating scientific discoveries. HPC is experiencing its own growth, with exascale computers and AI acceleration coming online at several supercomputer centers. AI/ML is in the midst of rapid growth of techniques and expansion into new application areas. This session will focus on current and emerging technologies in HPC, experimental workflows, and AI/ML techniques to help you incorporate them into your own research. Dr. D. Martin will provide "HPC Overview" followed by "Workflows" by Dr. C. Sweeney. "AI and ML" by Dr. A. Edelen will be followed by community discussions and questions from the audience.

Slides TUXE1 [11.946 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUXE3

Artificial Intelligence and Machine Learning for Particle Accelerators

A.L. Edelen
SLAC, Menlo Park, California, USA

As existing accelerator facilities are upgraded and new facilities come online, data volumes and velocity are increasing even with shorter data collection times. High-performance computing (HPC) systems doing simulation, data analytics and artificial intelligence/machine learning (AI/ML) are playing a major role in pre-experiment planning, design of experiments, real-time beam line and experiment analysis and control, and post-run data processing. Simulation and AI incorporated into experimental data analysis workflows are making efficient use of expensive facilities and accelerating scientific discoveries. HPC is experiencing its own growth, with exascale computers and AI acceleration coming online at several supercomputer centers. AI/ML is in the midst of rapid growth of techniques and expansion into new application areas. This session will focus on current and emerging technologies in HPC, experimental workflows, and AI/ML techniques to help you incorporate them into your own research. Dr. D. Martin will provide "HPC Overview" followed by "Workflows" by Dr. C. Sweeney. "AI and ML" by Dr. A. Edelen will be followed by community discussions and questions from the audience.

Slides TUXE3 [17.252 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)