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AI@LHC 2026: what's next?

25–29 May 2026
Peking University
Asia/Shanghai timezone

Contribution List

23 out of 23 displayed
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  1. 2. Welcome
    Qiang Li (Peking University (CN)), qinghong cao (Peking University)
    25/05/2026, 09:00
  2. 3. AI at the LHC
    Maurizio Pierini (CERN)
    25/05/2026, 09:30
  3. 4. AI for event simulation
    Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT))
    25/05/2026, 10:10
  4. 5. ML-based particle flow
    Farouk Mokhtar (Univ. of California San Diego (US))
    25/05/2026, 11:20
  5. 23. TBD
    Huaxing Zhu (ITP, PKU)
    25/05/2026, 11:55
  6. 6. Tutorial 1
    Huilin Qu (Tsung-Dao Lee Institute, Shanghai Jiao Tong University (CN))
    25/05/2026, 14:00
  7. 7. Keynote 1
    Peng-Shuai Wang (Wangxuan Institute of Computer Technology, Peking University)
    25/05/2026, 16:00

    Keynote talk
    https://wang-ps.github.io/

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  8. 8. Simulation-based inference for collider physics
    Jingjing Pan (KIT - Karlsruhe Institute of Technology (DE))
    26/05/2026, 09:00
  9. 10. Foundation Models for HEP
    Yulei Zhang (University of Washington (US))
    26/05/2026, 09:35
  10. 9. Simulation-based inference for precision neutrino physics [Remote]
    Mr Arsenii Gavrikov (HSE University)
    26/05/2026, 10:40
  11. 18. Keynote 2 [Remote]
    Weiyao Wang (Thinking Machines Lab)
    26/05/2026, 11:15

    Keynote talk
    https://sites.google.com/view/weiyaowang/home

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  12. 11. Tutorial 2: TBD
    26/05/2026, 14:00
  13. 12. New AI Infrastructure for Dr.Sai Agents at IHEP
    Hong Wang
    26/05/2026, 16:00
  14. 13. Jet tagging from DNN to Transformers
    Bingxuan Liu (Sun Yat-Sen University (CN))
    27/05/2026, 09:00
  15. 14. Jet tagging on the Edge
    Christopher Edward Brown (CERN)
    27/05/2026, 09:35
  16. 15. Anomaly Detection on the edge
    Maciej Mikolaj Glowacki (CERN)
    27/05/2026, 10:10
  17. 19. Higgs potential in the age of AI scaling
    Congqiao Li (Peking University (CN))
    27/05/2026, 11:15
  18. 22. Agent for multiple loop calculation
    Xiang Li (Peking University)
    27/05/2026, 11:50
    Oral talks

    The LHC now delivers percent-level precision across Higgs, top, and electroweak observables, yet the corresponding multi-loop theory predictions—essential for converting experimental measurements into constraints on the Standard Model and new physics—remain locked behind formidable expertise barriers. A full NNLO or N$^3$LO calculation requires navigating a fragmented ecosystem of specialized...

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  19. 1. Quantum autoencoder for anomalous couplings
    shuai zhang (LNNU)
    27/05/2026, 14:00
    Oral talks
    Lightening talks

    Given the success of the Standard Model (SM), any signals of physics beyond the Standard Model (NP) are expected to be small. Consequently, future searches for NP and precision tests of the SM will rely on high-luminosity collider experiments. The large volumes of data produced, together with the increased complexity of rare processes involving multiple final-state particles, pose significant...

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  20. 17. Learning Detector-Level Probability Distributions with Sliced Wasserstein Distance
    Chucheng Pan (Wuhan University (CN))
    27/05/2026, 14:15
    Oral talks
    Lightening talks

    High-energy physics is intrinsically a field that relies on the accurate modeling and comparison of high-dimensional probability distributions, arising from complex detector responses and multi-body final states. The Sliced Wasserstein Distance (SWD) provides a powerful and computationally efficient loss function tailored for high-dimensional probability distributions. By projecting high...

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  21. 21. “Hadron-in-jet” AI tagging to detect rare decays
    Linrui Chen (Wuhan University)
    27/05/2026, 14:30

    We investigate a novel class of boosted-object signatures at the LHC, where a high-pT fat jet contains an identifiable hadron or quarkonium state originating from rare or semi-exclusive decays.

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  22. 16. Talks&Closing
    27/05/2026, 16:30
  23. 20. Potential of di-Higgs observation via a calibratable jet-free HH->4b framework
    Tianyi Yang (Peking University (CN))

    We present a calibratable, jet-free framework that enhances the search significance of the flagship LHC channel HH→4b by more than a factor of five compared to existing approaches. The method employs a mass-decorrelated discriminant to identify h1h2→4b with variable h1,2 masses and a simultaneous estimator of (mh1,mh2), both derived from multiclass classification on all-particle inputs. The HH...

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