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SUMMARY:Machine Learning for Fundamental Physics School (ML4FP) 2026
DTSTART:20260601T130000Z
DTEND:20260605T220000Z
DTSTAMP:20260405T173200Z
UID:indico-event-17000@indico.global
DESCRIPTION:Speakers: Aishik Ghosh [Chair] (Georgia Institute of Technolog
 y)\, Elham E Khoda (University of British Columbia\, Vancouver)\, Yifan Ch
 en (SLAC National Accelerator  Laboratory (US))\, Melissa Quinnan (Univ. o
 f California San Diego (US))\, Dennis Noll (Stanford University)\n\n    \
 n \nThe Machine Learning for Fundamental Physics School (ML4FP) 2026 will
  be held at the Georgia Institute of Technology from June 1 to June 5\, 20
 26. The event is organized with support from Oak Ridge National Laboratory
  (ORNL)\, the National Energy Research Scientific Computing Center (NERSC)
  and the School of Physics at Georgia Tech.\nSpend a week in Atlanta learn
 ing a wide range of machine learning methods for solving problems in funda
 mental physics. The school places a strong emphasis on hands-on examples i
 nspired by applications in experimental particle physics and nuclear physi
 cs.\nPrevious iterations of the school include ML4FP 2025\, ML4FP 2024\, 
 the US ATLAS ML Training Program 2023\, and the US ATLAS ML Training Progr
 am 2022.\nThe US ATLAS ATC program supports domestic travel and accommodat
 ion for US ATLAS early career researchers. Additional funding from the DOE
  / ORNL supports domestic travel for other early career researchers. These
  participants will be offered lodging in Georgia Tech students apartments 
 located in Midtown Atlanta.\nApplication and registration details:\nApplic
 ation for the school can be made through this website (Registration tab on
  the left panel). Early career researchers requesting support for travel a
 nd accommodation will be required to ask their advisors to fill a very sho
 rt form in support of their interest and availability to attend the school
 . There is no fee to apply for the school.\nThe payment details will be pr
 ovided to selected applicants. The registration fee for in-person particip
 ation is USD 40. For remote participants who request GPU resources during 
 ML4FP tutorial hours\, the registration fee is  USD 10. The selection pro
 cess will take into account the relevance of a candidate's background\, in
 terests\, and preparedness\, and the balance of participants between exper
 iments and research domains. Beyond that\, the spots will be filled on a f
 irst-come\, first-served basis.\nApplication deadline: April 3rd 2026\nThe
 re are limited spots for in-person participation\, so apply soon!\nProgram
  overview:\nTentative topics:\n\nIntroduction to ML\nIntroduction to stand
 ard open-source ML packages\nOverview of ML in particle physics\nOverview 
 of network architectures\nGenerative models\nAnomaly detection\nNeural sim
 ulation-based Inference\nUncertainty quantification\nDifferentiable progra
 mming\nEfficient deployment of neural networks\n\n \nIndustry Talk:\nTBD\
 nComputing Resources:\nParticipants will be provided training accounts at 
 NERSC and access to GPUs.\nNetworking:\nPast schools have led to new resea
 rch collaborations. This year's school will be another opportunity for you
 ng ML enthusiasts to connect with veteran ML experts in HEP.\n \n\nTutori
 al GitHub:\n https://github.com/ml4fp/2026-gatech\nall the tutorial mater
 ials (except the experiment-specific sessions) will be publicly available 
 here\n \n \nZoom link will be added closer to the school\n \n \nJoinin
 g Link\nJoin the slack workspace to discuss and ask questions about the tu
 torials\, particularly for the remote participants.\n \n\n \nOrganizing 
 team:\nAishik Ghosh (Georgia Tech) [Chair]Yifan Chen (SLAC\, Stanford) [Ne
 utrino Liaison]Elham E Khoda (UBC)Dennis Noll (SLAC\, Stanford) [ATLAS Lia
 ison]Melissa Quinnan (UCSD) [CMS Liaison]Steering Committee:Sascha Diefenb
 acher (Heidelberg)Steven Farrell (NERSC/LBNL)Aishik Ghosh (Georgia Tech) 
 Shih-Chieh Hsu (UW)Elham E Khoda (UBC)Benjamin Nachman (SLAC\, Stanford)Da
 niel Whiteson (UCI)\n \n\nOur Partners:\n      \n\n \n\nhttps://indic
 o.global/event/17000/
IMAGE;VALUE=URI:https://indico.global/event/17000/logo-201986585.png
LOCATION:Marcus Nanotechnology Building
URL:https://indico.global/event/17000/
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