Machine Learning (ML) techniques have been adopted at all levels of applications, including experimental design optimization, detector operations and data taking, physics simulations, data reconstruction, and physics inference. Neutrino Physics and Machine Learning (NPML) is dedicated to identifying, reviewing, and building future directions for impactful research topics for applying ML techniques in Neutrino Physics.
We invite both individual speakers as well as representatives from a large collaboration in the neutrino community to share the development and applications of ML techniques. Speakers from outside neutrino physics are welcome to make contributions.
Registration: use this registration form.
Fee: Students: 250 CHF (Payment Link for Students), Rest: 350 CHF (Payment Link for Non-Students).
Talk/Poster Request: please submit your contribution request in this Indico page (call for abstracts, deadline passed on April 14).
NPML24 is designed as an on-site event to encourage interactive discussions. Nevertheless, all presentations will be accessible remotely on the Indico site. We thank you for your understanding.
Local Committee:
- Saúl Alonso-Monsalve (ETH Zurich)
- Marta Babicz (University of Zurich)
- Davide Sgalaberna (ETH Zurich)
- Leigh Whitehead (University of Cambridge)
International Scientific Committee:
- Kazuhiro Terao (SLAC)
- Patrick de Perio (Kavli IPMU)
- Jianming Bian (UC Irvine)
- Adam Aurisano (University of Cincinnati)
- Nick Prouse (Imperial College London)
- Corey Adams (ANL)
- Taritree Wongjirad (Tufts University)
- Aobo Li (UC San Diego)
Organization Committee [contact]
