JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{edelen:napac2022-mopa55,
author = {J.P. Edelen and D.T. Abell and K.A. Brown and D.L. Bruhwiler and S. Calder and S.J. Coleman and N.M. Cook and A. Diaw and A.L. Edelen and J.A. Einstein-Curtis and C.C. Hall and C.M. Hoffmann and E.-C. Huang and M.C. Kilpatrick and B. Nash and B.D. O’Shea and P. Piot and I.V. Pogorelov and R.J. Roussel and C. Tennant},
% author = {J.P. Edelen and D.T. Abell and K.A. Brown and D.L. Bruhwiler and S. Calder and S.J. Coleman and others},
% author = {J.P. Edelen and others},
title = {{Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry}},
& booktitle = {Proc. NAPAC'22},
booktitle = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
pages = {164--169},
eid = {MOPA55},
language = {english},
keywords = {controls, simulation, software, operation, framework},
venue = {Albuquerque, NM, USA},
series = {International Particle Accelerator Conference},
number = {5},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2022},
issn = {2673-7000},
isbn = {978-3-95450-232-5},
doi = {10.18429/JACoW-NAPAC2022-MOPA55},
url = {https://jacow.org/napac2022/papers/mopa55.pdf},
abstract = {{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.}},
}