BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:QUC Summer School on “A.I. in High Energy Physics\,” Part II\,
  Recent Development in AI
DTSTART:20260824T000000Z
DTEND:20260828T100000Z
DTSTAMP:20260817T010600Z
UID:indico-event-1627722@indico.cern.ch
CONTACT:pjh999@kias.re.kr
DESCRIPTION:Speakers: Pyungwon Ko (KIAS (Korea Institute for Advanced Stud
 y))\, Michael Spannowsky (IPPP Durham)\, Myeonghun Park (Seoultech)\, Minh
 o Son\, Seung J. Lee (Korea Institute for Advanced Study)\n\nThis year's s
 chool will highlight emerging developments in artificial intelligence (AI)
  and machine learning. There will be active interaction with the lecturers
 \, fostering discussions that may naturally develop into collaborative res
 earch projects. The school is intended for students and postdoctoral resea
 rchers seeking to deepen their understanding of the field and explore emer
 ging research directions. \nThe registration fee is 100\,000 KRW and shou
 ld be paid in cash at on-site registration.\nLectures\n\nJack Y. Araz (Cit
 y St. George’s\, University of London)\n\nArtificial Intelligence for Hi
 gh-Energy Physics\nThis lecture series provides an introduction to artific
 ial intelligence for high-energy physics. It covers supervised and unsuper
 vised learning\, generative models\, simulation-based inference\, and mode
 rn architectures for particle-physics data. Complementary lectures discuss
  symmetry-aware learning\, uncertainty quantification\, and the integratio
 n of theoretical knowledge into machine-learning methods.\n\nSven Krippend
 orf (Cambridge U. DAMTP)\n\nAgentic AI for Theoretical Physics: From Resea
 rch Questions to Verifiable Results\nThis lecture series provides an intro
 duction to agentic AI for theoretical physics. It covers coding agents\, a
 gentic research workflows\, literature and tool use\, and methods for veri
 fying and reproducing AI-assisted results. Complementary lectures introduc
 e theoretical foundations of neural networks\, including Gaussian-process 
 and neural-tangent-kernel limits. The accompanying hands-on tutorials expl
 ore research projects with agents.\n\nMichael Spannowsky (KIT\, Karlsruhe)
 \n\nQuantum Machine Learning for Fundamental Physics\nThis lecture series 
 provides an introduction to quantum machine learning with applications to 
 fundamental physics. It covers quantum data encoding\, variational quantum
  circuits\, quantum kernels\, quantum neural networks\, and hybrid quantum
 -classical learning methods.\n \n\nhttps://indico.cern.ch/event/1627722/
LOCATION:Building #1\, 1503 (KIAS)
URL:https://indico.cern.ch/event/1627722/
END:VEVENT
END:VCALENDAR