"ML at HEP workshop" in Japan 2024

Kobayashi hall (KEK (High Energy Accelerator Research Organization))

Kobayashi hall

KEK (High Energy Accelerator Research Organization)

1-1 Oho, Tsukuba city, Ibaraki 305-0801, Japan

We are delighted to announce that our second annual "Machine Learning at HEP workshop" will take place between 9th-10th January 2024 at High Energy Accelerator Research Organization (KEK) in Japan.

In the previous workshop ( KEK indico link ) , we successfully overviewed the state-of-the-art Machine Learning (ML) applications to high energy physics, and had lively discussions about the directions of ML developments in the field. The scope of this workshop is to highlight recent progresses in the state-of-the-art ML applications to high energy physics and astrophysics through several talks by world-leading researchers, and to overview the related researches progressing in Japan.

The workshop format is hybrid (in-person at KEK (strongly preferred for lively discussions) and online via zoom (possible)). Long review presentations are given by invited speakers, while a session of several contribution talks is also scheduled. Please indicate your intention to present a contribution talk on your ML-related work via the registration form by December 15th. Domestic travel and accommodation support can be provided to a limited number of presenters. If you would like to request it, please contact us via the LOC mailing list by December 15th.


Registration for participation was open at https://conference-indico.kek.jp/event/253/registrations/ , and  has been closed on December 25th. No registration fee is necessary. Application for contribution talks has been closed.

Confirmed Invited Speakers:

  • Matt Buckley (Rutgers)
  • Gregor Kasieczka (Hamburg)
  • Shiro Ikeda (The Institute of Statistical Mathematics)
  • François Lanusse (Flatiron Institute / CNRS)
  • Sung Hak Lim (Rutgers) 
  • Vinicius Mikuni (LBL)
  • Mariel Pettee (LBL)
  • Huilin Qu (CERN)

This workshop will be hosted co-organized by KEK WPI-QUP (International Center for Quantum-field Measurement Systems for Studies of the Universe and Particles) and IPNS (Institute of Particle and Nuclear Studies), and supported by KEK WPI-QUP, JSPS Core-to-Core Program "AI-Smart" and JSPS Grant-in-Aid for Transformative Research Areas (A) "Machine Learning Physics".

Program organizers: Yuji Chinone (KEK), Hironao Miyatake (Nagoya U), Yu Nakahama (KEK), Mihoko Nojiri (KEK), Junichi Tanaka (U Tokyo)

Local organisers: Yuji Chinone (KEK), Daniel Jeans (KEK), Gaku Mitsuka (KEK), Kunihiro Nagano (KEK), Kodai Matsuoka (KEK), Yu Nakahama (KEK), Mihoko Nojiri (KEK)

  • Abdulkadir Mohamed Gedi
  • Abner Soffer
  • Adil Jueid
  • Adrian Bayer
  • Ahmed Hammad
  • Akimasa Ishikawa
  • Akira Harada
  • Aman Pavan Salikar
  • Andrew Bakich
  • Anthony Little
  • Arkodip Biswas
  • asuka shiomi
  • Bela Urbschat
  • Bilge Selcen Solak
  • Cecilia Antonioli
  • Chanho Kim
  • Chris Nagele
  • Cristina Martellini
  • Daniel Jeans
  • Darius Faroughy
  • Francois Lanusse
  • Gaku Mitsuka
  • Gregor Kasieczka
  • Hiroaki Yamamoto
  • Hironao Miyatake
  • Hisaaki Shinkai
  • Huilin Qu
  • Hyuna Kim
  • Isai Roberto Sotarriva Alvarez
  • Jigar Patel
  • Jing-Ge Shiu
  • Jitendra Kumar
  • Junichi Kanzaki
  • Junichi Mori
  • Junichi Tanaka
  • Junpei Maeda
  • Jyotirmoi Borah
  • Kavita Lalwani
  • Kazuyuki Akitsu
  • Kentaro Nagamine
  • Kentarou Mawatari
  • Kitayama Atsuki
  • Kodai Matsuoka
  • Koji Hara
  • Koji Terashi
  • Krishnakumar Ravindran
  • Kunihiro Nagano
  • Kyotaro Nishi
  • Leo Piilonen
  • Livio Lanceri
  • Logan Benninghoff
  • Louis Vaslin
  • Lucia Kapitanova
  • Masahiko Saito
  • Masahiro Morinaga
  • Masashi Hazumi
  • Matthew Barrett
  • Maxime Paillassa
  • Miu Kashiwazaki
  • Motoi ENDO
  • Myeonghun Park
  • N Sushree Ipsita
  • Naomi Tsuji
  • Naveen Baghel
  • Nobuyuki Sakai
  • Pankaj Saha
  • Rahool Barman
  • Raymundo Ramos
  • Riku Nomaru
  • Risako Tagami
  • Rishav Pandey
  • Saurabh Sandilya
  • Seiji Kasai
  • Selcuk Bilmis
  • Sergei Zakharov
  • Shingo Hirano
  • Shinnosuke Kato
  • Shubhangi Maurya
  • Shun Watanuki
  • Souvik Maity
  • Suchetha Cooray
  • Suneel Dutt
  • Sung Hak Lim
  • Taichi Sakai
  • Taikan Suehara
  • Takahiro Matsumoto
  • Takane Sano
  • Takashi Hamana
  • Tatsuki Fujiwara
  • Tatsuya Masubuchi
  • Thanh Nguyen
  • Thomas Browder
  • Tijmen de Haan
  • Timo Schellhaas
  • Tomoe Kishimoto
  • Tomomi Sunayama
  • Trevor Shillington
  • Vinicius Mikuni
  • Vismaya V S
  • Xu Dong
  • Ya-Juan Zheng
  • Yannik Buch
  • Yasuo Arai
  • Yi Zhang
  • Yo SATO
  • Yu Nakazawa
  • Yui Murata
  • Yuji Chinone
  • Yutaro Iiyama
  • Yuto Ichinohe
  • Yuxin Liu
  • Zihan Wang
  • Tuesday, January 9
    • 12:30 PM 1:00 PM
      Registration 30m
    • 1:00 PM 1:05 PM
      Opening 5m
      Speaker: Yu NAKAHAMA (KEK)
    • 1:05 PM 1:45 PM
      Data Science for Astronomy 40m
      Speaker: Shiro Ikeda (The Institute of Statistical Mathematics)
    • 1:45 PM 2:25 PM
      Review: Low-level object reconstruction and simulation using ML 40m
      Speaker: Vinicius Mikuni (LBL)
    • 2:25 PM 3:05 PM
      ML for Jet Tagging: Status and Prospects 40m
      Speaker: Huilin Qu (CERN)
    • 3:05 PM 3:10 PM
      Group Photo 5m
    • 3:10 PM 3:35 PM
      Coffee 25m
    • 3:35 PM 4:15 PM
      Dark Matter Applications of Machine Learning 40m
      Speaker: Matt Buckley (Rutgers University)

      Abstract: Machine learning algorithms provide powerful new windows into complicated and high-dimensional datasets. Of particular interests are normalizing flows, which estimate the phase space density of data in an unsupervised manner. These new techniques arrive concurrently with an era of Big Data in astrophysics, as large sky surveys provide a wealth of data about the structure of our own Galaxy and the Universe beyond. In this talk, I discuss applications of normalizing flows to Gaia data, which measures the position and velocity of the nearest 1.5 billion bright stars (30 million with full 6D kinematics): mapping the dark matter distribution within 4 kpc of the Sun, searching for stellar streams in the Galaxy, and generating synthetic astronomical datasets.

    • 4:15 PM 4:35 PM
      Flow-Matching models for the LHC 20m
      Speaker: Darius Faroughy (Rutgers University)
    • 4:35 PM 4:55 PM
      AI Methods in Galaxy Formation 20m
      Speaker: Suchetha Cooray (NAOJ)
    • 4:55 PM 5:15 PM
      Multi-Scale Cross-Attention Transformer Encoder for Event Classification 20m
      Speaker: Ahmed Hammad (KEK)
    • 5:15 PM 5:35 PM
      A data-driven and model-agnostic approach to solving combinatorial assignment problems in searches for new physics 20m
      Speaker: Javier Montejo (IFAE Barcelona & QUP)
  • Wednesday, January 10
    • 9:30 AM 10:10 AM
      Prospects of LLMs for Fundamental Physics 40m
      Speaker: Mariel Pettee (LBL)
    • 10:10 AM 10:50 AM
      Review and Perspective on AI for Observational Cosmology 40m
      Speaker: François Lanusse (Simons Foundation/CNRS)
    • 10:50 AM 11:10 AM
      Extracting optimal information from upcoming cosmological surveys 20m
      Speaker: Adrian Bayer (Princeton)
    • 11:10 AM 11:30 AM
      Cosmosage: a natural-language assistant for cosmology 20m
      Speaker: Tijmen de Haan (KEK)
    • 11:30 AM 1:00 PM
      Lunch 1h 30m
    • 1:00 PM 1:40 PM
      Using AI for unsupervised discovery in particle physics 40m
      Speaker: Gregor Kasieczka (Hamburg)
    • 1:40 PM 2:00 PM
      Model-independence of machine-learning-based mass reconstruction for inclusive multi-jet search 20m
      Speaker: Takane Sano (Kyoto University)
    • 2:00 PM 2:20 PM
      Jet Classification using High-Level Features from Anatomy of Top Jets 20m
      Speaker: Sung Hak Lim (Rutgers University)
    • 2:20 PM 2:40 PM
      Development of Anomaly Detection techniques applied to the building and Quality Control of ATLAS new silicon tracking detector 20m
      Speaker: Louis Vaslin (KEK QUP)
    • 2:40 PM 3:00 PM
      Pre-training strategy using real particle collision data for event classification in collider physics 20m
      Speaker: Tomoe Kishimoto (KEK)
    • 3:00 PM 3:30 PM
      Coffee break 30m
    • 3:30 PM 3:50 PM
      Returning CP-observables to the frames they belong (unfolding) 20m
      Speaker: Rahool Barman (IPMU)
    • 3:50 PM 4:10 PM
      A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies 20m
      Speaker: Chris Nagele (University of Tokyo)
    • 4:10 PM 4:30 PM
      Improving the diphoton event selection with GNNs 20m
      Speaker: Isai Roberto Sotarriva Alvarez (TokyoTech)
    • 4:30 PM 4:50 PM
      Machine-learning-assisted beam tuning at the KEK Linac and prospects for SuperKEKB 20m
      Speaker: Shinnosuke Kato (University of Tokyo)
    • 4:50 PM 5:10 PM
      Development of a hardware trigger using machine learning in the Belle II experiment 20m
      Speaker: Riku Nomaru (University of Tokyo)
    • 5:10 PM 5:20 PM
      Closing 10m
      Speaker: Junichi Tanaka (The University of Tokyo )