The Fast Machine Learning for Science Conference 2025 will be hosted by ETH Zurich September 1-5th, 2025.
As experimental methods continue to evolve, generating increasingly complex and high-resolution datasets, machine learning (ML) is becoming an essential tool across numerous scientific disciplines. This conference will explore emerging ML methods and their applications in scientific discovery, focusing on processing technologies and strategies to accelerate deep learning and inference.
Topics
Topics include, but are not limited to:
Machine Learning Algorithm Design & Optimization
- Novel efficient architectures
- Hyperparameter optimization and tuning
- Model compression (quantization, sparsity)
- Hardware/software co-design for ML efficiency
Accelerated Inference & Real-Time Processing
- Low-latency ML for scientific experiments
- FPGA/GPU-based ML acceleration
- ML for trigger systems and data acquisition
- On-detector and edge inference
Scalable & Distributed ML Systems
- Cloud-based, accelerated ML processing
- Distributed inference
- Acceleration-as-a-service
Advanced Hardware & Computing Architectures
- Specialized AI accelerators
- Heterogeneous computing platforms for ML
- Beyond CMOS
Scientific Applications of Fast ML
- High-energy physics, astrophysics and astronomy
- Space science and satellite-based ML
- Genomics and medical imaging
- Climate and environmental modeling
- Material Science
- Robotics
Important Deadlines
- Abstract Submission: July 1, 2025
- Registration Deadline: August 1, 2025
We welcome abstracts for:
- Scientific talks
- Posters
- 2-4 hour Monday tutorials
- 2-3 hour Wednesday topical (birds-of-a-feather) sessions
More information and registration details will follow. We look forward to welcoming you in Zurich this September!
Best regards,
On behalf of the Organizers
Scientific Committee
- Thea K. Årrestad (ETH Zürich)
- Javier Duarte (UCSD)
- Phil Harris (MIT)
- Burt Holzman (Fermilab)
- Scott Hauck (U. Washington)
- Shih-Chieh Hsu (U. Washington)
- Sergo Jindariani (Fermilab)
- Mia Liu (Purdue University)
- Allison M. Deiana (Southern M. U.)
- Mark Neubauer (U. Illinois U-C)
- Jennifer Ngadiuba (Fermilab)
- Maurizio Pierini (CERN)
- Sioni Summers (CERN)
- Alex Tapper (Imperial College)
- Nhan Tran (Fermilab)
Organising Committee
- Thea K. Årrestad (ETH Zürich) - Chair
- Marius Köppel (ETH Zürich) - Co-Chair
- Cristina Botta (CERN/UZH)
- Annapaola De Cosa (ETH)
- Patrick Odagiu (ETH Zürich)
- Maurizio Pierini (CERN)
- Anna Sfyrla (UniGe)
- Sioni Summers (CERN)
- Jennifer Zollinger (ETH Zürich)
