Author: Hoffmann, C.M.
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
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