We are pleased to announce a four-day event Fast Machine Learning for Science, which will be hosted by Imperial College London from September 25-28, 2023. The first three days will be workshop-style with invited and contributed talks. The last day will be dedicated to technical demonstrations and satellite meetings. The event will be hybrid with an in-person, on-site venue and the possibility to join virtually. For those attending in-person there will be a social reception during the evening of Monday 25th, and a dinner on Wednesday 28th.
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: 14th August (extended!)
Registration deadline: 1st September
Organising Committee:
Sunita Aubeeluck
Robert Bainbridge
David Colling
Marvin Pfaff
Wayne Luk
Andrew Rose
Sioni Summers (co-chair)
Alex Tapper (co-chair)
Yoshi Uchida
Scientific Committee:
Thea Aarrestad (ETH Zurich)
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 McCarn Deiana (Southern Methodist University)
Mark Neubauer (U. Illinois Urbana-Champaign)
Jennifer Ngadiuba (Fermilab)
Maurizio Pierini (CERN)
Sioni Summers (CERN)
Alex Tapper (Imperial College)
Nhan Tran (Fermilab)
