Machine Learning at LHC

Over the past years, machine learning (ML) has become a hot topic in particle physics. In particular, there is rapid progress in particle identification, event reconstruction, fast simulation, and jet flavor tagging at the LHC. In and beyond these fields ML applications are constantly expanding and we believe that the specific particle physics applications can also help other research fields.

In this workshop, we bring together world experts actively working on the application of ML at the LHC, both in theory and experiment, to discuss the current status and the main challenges of machine learning in particle physics. In addition, we plan to discuss possible new idea and new directions. We will mainly focus on the ML application at the LHC but not limited and open to the full ML community, therefore, we hope many physicists from various research fields will participate in this workshop.

The workshop will include lectures for graduate students and young postdocs, and research talks by the invited speakers on the above issues. We will also have a half-day workshop for participants to give a short talk.

The invited speakers:
  Johann Brehmer (NewYork Univ.)
  Anja Butter (Heidelberg Univ.)
  Marat Freytsis (Rutgers Univ.)
  Michael Kagan (SLAC, ATLAS)
  SungHak Lim (KEK)
  Tao Liu (HKUST)
  Benjamin Nachman (LBNL, ATLAS)
  Tilman Plehn (Heidelberg Univ.)
  Joe Sato (Saitama Univ.)
  David Shih (Rutgers Univ.)
  Koji Terashi (ICEPP, ATLAS) LOC: 
  Junji Hisano (KMI, Nagoya Univ.)
  Toru Iijima (KMI, Nagoya Univ.)
  Yu Nakahama (KMI, Nagoya Univ.)
​  Michihisa Takeuchi (KMI, Nagoya Univ., chair) 

Advisary committee:
  Mihoko Nojiri (KEK)
  Tilman Plehn (Heidelberg Univ.)

If you need to contact to the LOC, please send e-mail to MLatLHC@kmi.nagoya-u.ac.jp

This workshop is organized and sponsored by
  KMI
  WPI-next (http://wru.hepl.phys.nagoya-u.ac.jp/index.html)
  Grant-in-Aid: 19H04613, 18K03611, 16H03991