About the conference:
The 5th Neutrino Physics and Machine Learning (NPML 2026) will take place at the University of California, Irvine, USA. The NPML conference series are dedicated to identifying new opportunities, developing and sharing firm knowledge base, and building the future visions for impactful Artificial Intelligence and Machine Learning (AI/ML) research for neutrino physics.
We look forward to your contributions to share the latest AI/ML research advancements at all levels of applications in neutrino physics, including experimental design optimization, detector operations and calibrations, physics simulations, data reconstruction, and physics inference.
We invite both individual speakers as well as representatives from a large collaboration in the neutrino community. Speakers from outside neutrino physics are also welcome to make contributions: your contributions will bring new insights and help us develop interdiciplinary research collaborations.
Key Information and Deadlines
- Registration Fee
- Location:
- Interdisciplinary Science and Engineering Building, UC Irvine
- Dates:
- June 15th to 19th
- March 31st 2026
- Deadline for Early Registration
- 200 USD (regular registration)
- 150 USD (student registration)
- Deadline for Financial Support
- Registration fee waivier
- Accommodations ($150/night for 5 nights)
- Deadline for Early Registration
- April 30th 2026
- Deadline for oral/poster presentation
- Apply within the registration form
- Formal title/abstract deadline later
- Deadline for Standard Registration
- 300 USD (regular registration)
- 200 USD (student registration)
- Deadline for oral/poster presentation
- May 15th 2026
- Formal title/abstract deadline
Session Tracks for Talks
- AI/ML R&D - focus on technical developments (i.e. not analysis)
- Lessons Learned and Challenges - hard-learned failures, unaddressed challenges behind a success story
- Public Datasets and Olympics - sharing existing datasets, proposal for new releases and open data challenges
- Applications in Experiments - Physics impacts in experiments enabled by AI tools
- Shareable AI Tools - tools that can be / are shared across experiments (i.e. AI models, AI-enabled workflows, etc.)
- Accelerated, Scalable Compute - GPU programming, distributed compute, fast simulation, scalable designs
When you register, please pick the track in which you would like to give your presentation. There is no track specification for posters.
Contributing Talks/Posters:
Please indicate your interest in the registration form. You do not need to submit a formal title nor abstract at the time of registration - this is to motivate early registration as soon as possible.
The submission deadline of a formal title and abstract is April 30th. When the official title and abstract are ready, please submit them from the Call for Abstracts page.
At NPML, we strongly encourage speakers of oral presentation to also consider a poster presentation which allows participants to interact more in depth with you and learn about your research.
Local Organizational Committee
- Jianming Bian
- Pierre Baldi
- Aobo Li
- Kazuhiro Terao
International Organization Committee
- Callum Wilkinson (LBNL)
- Roger Huang (LBNL)
- Francois Drielsma (SLAC)
- Hirohisa Tanaka (SLAC)
- Adam Aurisano (Cincinnati)
- Xin Qian (BNL)
- Zelimir Djurcic (ANL)
- Leigh Whitehead (Cambridge)
- Saul Alonso (ETH)
- Marta Ewelina Babicz (University of Zurich)
- Benda Xu (Tsinghua University)
