The Fast Machine Learning for Science Conference 2025 will be hosted by ETH Zurich September 1-5th, 2025.
As advances in experimental methods create growing datasets and higher resolution and more complex measurements, machine learning (ML) is rapidly becoming the major tool to analyze complex datasets over many different disciplines. Following the rapid rise of ML through deep learning algorithms, the investigation of processing technologies and strategies to accelerate deep learning and inference is well underway. We envision this will enable a revolution in experimental design and data processing as a part of the scientific method to greatly accelerate discovery. This workshop is aimed at current and emerging methods and scientific applications for deep learning and inference acceleration, including novel methods of efficient ML algorithm design, ultrafast on-detector inference and real-time systems, acceleration as-a-service, hardware platforms, coprocessor technologies, distributed learning, and hyper-parameter optimisation.
Abstract submission deadline: 1st August
Registration deadline: 1st September
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
Patrick Odagiu (ETH Zürich)
Maurizio Pierini (CERN)
Anna Sfyrla (UniGe)
Sioni Summers (CERN)
Jennifer Zollinger (ETH Zürich)
